%% This BibTeX bibliography file was created using BibDesk.
%% http://bibdesk.sourceforge.net/
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@string{acmcs = {ACM Computing Surveys}}
@string{dcg = {Discrete and Computational Geometry}}
@string{focs = {Symposium on Foundations of Computer Science}}
@string{focs90 = {Proc. 31st IEEE Symp. on Foundations of Computer Science}}
@string{form = {short}}
@string{ieeecom = {IEEE Transactions on Communications}}
@string{ieeeit = {IEEE Transactions on Information Theory}}
@string{jmlr = {Journal of Machine Learning Research}}
@string{pami = {IEEE Transactions on Pattern Analysis and Machine Intelligence}}
@string{prl = {Pattern Recognition Letters}}
@string{proc = {Proceedings of the }}
@string{socg = {Annual Symposium on Computational Geometry}}
@string{soda = {Annual ACMSIAM Symposium on Discrete Algorithms}}
@string{stoc = {Annual ACM Symposium on Theory of Computing}}
@string{talg = {ACM Transactions on Algorithms}}
@techreport{Clarkson77,
Address = {Claremont, CA},
Author = {Kenneth L. Clarkson},
Institution = {Claremont Graduate School},
Title = {Implementation of a Model of ${CO}_2$ Diffusion in the Midtroposphere},
Type = {Technical Report},
Year = {1977}}
@inproceedings{Clarkson81,
Address = {Maclean, VA: Science Applications, Inc.},
Author = {Kenneth L. Clarkson},
Booktitle = {Proceedings of the DARPA Image Understanding Workshop},
Title = {A Procedure for Camera Calibration},
Year = {1981}}
@incollection{Clarkson82,
Address = {Los Altos, CA},
Author = {Kenneth L. Clarkson},
Booktitle = {The Handbook of Artificial Intelligence},
Category = {Survey},
Editor = {P. Cohen and E. Feigenbaum},
Publisher = {William Kaufman, Inc.},
Title = {Grammatical Inference},
Year = {1982}}
@article{Clarkson83,
Author = {Kenneth L. Clarkson},
Journal = {Information Processing Letters},
Month = {January},
Pages = {2325},
Title = {A Modification of the Greedy Algorithm for Vertex Cover},
Volume = {16},
Year = {1983}}
@inproceedings{ann,
Address = {Tucson, AZ},
Author = {Kenneth L. Clarkson},
Booktitle = {FOCS '83: } # proc # {TwentyFourth } # focs,
Month = {November},
Note = {Included in PhD Thesis},
Title = {Fast Algorithms for the All Nearest Neighbors Problem},
Url = {thesis_abs.html},
Year = {1983},
Abstract = {
We present new algorithms for the all nearest neighbors problem:
Given a set `S` of `n` sites (points) in `d`dimensional space, find the
nearest neighbors in set `S` of each site in `S`.
Our results:

An algorithm for solving the all nearest neighbors problem in `O(n\log\sigma)`
time, where `\sigma` is the ratio of the distance between the farthest pair
of sites to the distance between the closest pair of sites. A similar
algorithm is described for finding all `k`'th nearest neighbors.

An algorithm for building a celltree, a compressed form of digital trie,
in `O(n\log n)` probabilistic time. The logarithm, floor, and bitwise
exclusiveor functions are assumed available at unit cost.

An algorithm for solving the all nearest neighbors problem in `O(n)` worstcase
time, given a celltree for the sites.

An algorithm for building a celltree in linear expected time, assuming the
sites are independently identically distributed random variables, with
an unknown probability density function obeying some very weak conditions.
},
Annote = {
A "celltree" is now more commonly
called a "compressed quadtree" or "compressed hyperoctree", and this paper is apparently
the first appearance of such a construction. Vaidya
refined the
algorithm here to avoid randomness and bittwiddling, at some cost
in dependence on `d`; his algorithm, and this one, and
that of Gabow, Bentley, and Tarjan, all use the same basic
geometrical observation, which implies that the total
number of nearest neighbors is `O(n)`, even up to approximate neighbors.
Callahan and Kosaraju
used similar ideas for "wellseparated pairs decomposition".
The `\sigma` ratio is now more commonly called the spread.
},
BdskUrl1 = {thesis_abs.html}}
@inproceedings{Clarkson84,
Address = {Washington, DC},
Author = {Kenneth L. Clarkson},
Booktitle = {STOC '84: } # proc # {Sixteenth } # stoc,
Month = {April},
Note = {Included in PhD Thesis},
Title = {Fast Expectedtime and Approximation Algorithms for Geometric Minimum Spanning Trees},
Url = {thesis_abs.html},
Year = {1984},
BdskUrl1 = {thesis_abs.html}}
@phdthesis{Clarkson85,
Author = {Kenneth L. Clarkson},
Month = {January},
School = {Stanford University},
Title = {Algorithms for Closestpoint Problems},
Url = {thesis_abs.html},
Year = {1985},
Abstract = {
This dissertation reports a variety of new algorithms for solving
closestpoint problems. The input to these algorithms is a set or
sets of points in `d`dimensional
space, with an associated `L_p` metric. The problems considered
are:
 The all nearest neighbors
problem. For point set `A`, find the nearest neighbors in `A`
of each point in `A`.
 The nearest foreign neighbor
problem. For point sets `A` and `B`, find the closest point
in `B` to each point in `A`.
 The geometric minimum spanning
tree problem. For point set `A`, find the minimum spanning tree
for the complete weighted undirected graph associated with `A`,
where the vertices of the graph
correspond to the points of `A`,
and the weight of an edge is the distance between the points defining
the edge.
These problems arise in routing, statistical classification, data
compression, and other areas. Obvious algorithms for them require
a running time quadratic in `n`,
the number of points in the input. In many cases they can be
solved with algorithms requiring `O(n \log ^{O(1)} n)` time.
In this work, approximation algorithms for some cases of these problems
have been found. For example, for the minimum spanning tree
problem with the `L_1`
metric, an algorithm has been devised that requires `O(n \log^d (1/\rho))`
time to find a spanning
tree with weight within `1+\rho`
of the minimum. Several other algorithms have been found with
time bounds dependent on `\log(1/\rho)`
for attaining error `\rho`.
Algorithms have also been found that require linear expected time, for
independent identically distributed random input points with a
probability density function satisfying weak conditions. One such
algorithm depends on the fact that under certain conditions, values
that are identically distributed, but dependent, can be bucket sorted
in linear expected time.
An algorithm has been found for the all nearest neighbors problem that
requires `O(n \log n)` expected time for any input set
of points, where the expectation is on the random sampling performed by
the algorithm. This algorithm involves the construction of a new
data structure, a compressed form of digital trie.
},
BdskUrl1 = {thesis_abs.html}}
@article{rpo,
Appearedas = {A Probabilistic Algorithm for the Post Office Problem},
Appearedin = {STOC '85: } # proc # {Seventeenth } # stoc,
Author = {Kenneth L. Clarkson},
Ayear = {1985},
Journal = sicomp,
Pages = {830847},
Pdf = {rpo/p.pdf},
Ps = {rpo/p.ps},
Title = {A Randomized Algorithm for ClosestPoint Queries},
Year = {1988},
Abstract = {
An algorithm for closestpoint queries is given.
The problem is this: given a set `S` of `n` points in
`d`dimensional space, build a data structure so that
given an arbitrary query point `p`, a closest point in `S`
to `p` can be found quickly. The measure of distance is
the Euclidean norm. This is sometimes called the
postoffice problem [Kn]. The new data structure
will be termed an RPO tree, from Randomized Post
Office. The expected time required to build an RPO tree is
`O(n^{\lceil d/2\rceil (1+ \epsilon )} )`, for any fixed `\epsilon >
0`, and a query can be answered in `O(\log n )` worstcase
time. An RPO tree requires `O(n^{\lceil d/2\rceil (1+ \epsilon )}
)` space in the worst case. The constant factors in
these bounds depend on `d` and `\epsilon`. The bounds
are averagecase due to the randomization employed by
the algorithm, and hold for any set of input points.
This result approaches the `\Omega(n^{\lceil d/2\rceil } )` worstcase
time required for any algorithm that constructs the
Voronoi diagram of the input points, and is a considerable
improvement over previous bounds for `d>3`. The main
step of the construction algorithm is the determination
of the Voronoi diagram of a random sample of the sites,
and the triangulation of that diagram.
},
Annote = {
A key result proven and used
is that with high probability, every Delaunay ball of a random
sample of the sites each contains few sites of the full set.
This is the first appearance of the kind of analysis done in more generality
here; the analysis essentially reduces to the
observation that there are `r^{O(1)}` balls associated with
a sample of size `r`, that might be Delaunay, and an exponentially
small probability that a given such ball with many points in it
would be Delaunay in the sample, and finally, the union bound.
}}
@article{Clarkson86,
Author = {Kenneth L. Clarkson},
Journal = {Information Processing Letters},
Month = {January},
Pages = {2124},
Title = {Linear Programming in ${O(n 3^{d^2})}$ Time},
Volume = {22},
Year = {1986}}
@article{rs,
Appearedas = {Further Applications of Random Sampling to Computational Geometry},
Appearedin = {STOC '86: } # proc # {Eighteenth } # stoc,
Author = {Kenneth L. Clarkson},
Ayear = {1986},
Dvi = {rs/p.dvi},
Journal = dcg,
Pages = {195222},
Pdf = {rs/p.pdf},
Ps = {rs/p.ps},
Title = {New Applications of Random Sampling to Computational Geometry},
Volume = {2},
Year = {1987},
Abstract = {
This paper gives several new demonstrations of
the usefulness of random sampling techniques in
computational geometry. One new algorithm creates a
search structure for arrangements of hyperplanes by
sampling the hyperplanes and using information from
the resulting arrangement to divide and conquer.
This algorithm requires `O(s ^ {d + \epsilon})`
expected preprocessing time to build a search structure
for an arrangement of `s` hyperplanes in `d` dimensions.
The expectation, as with all expected times reported here,
is with respect to the random behavior of the algorithm,
and holds for any input. Given the data structure, and a
query point `p`, the cell of the arrangement containing
`p` can be found in `O( \log s)` worstcase time.
(The bound holds for any fixed `\epsilon >0`, with
the constant factors dependent on `d` and `\epsilon`.)
Using pointplane duality, the algorithm may be used for
answering halfspace range queries. Another algorithm finds
random samples of simplices to determine the separation
distance of two polytopes. The algorithm uses expected
`O(n ^{\lfloor d/2\rfloor} )` time, where `n` is the total number
of vertices of the two polytopes. This matches previous
results [DoK] for the case `d=3` and extends them.
Another algorithm samples points in the plane to determine
their order `k` Voronoi diagram, and requires expected
`O(s^{1+\epsilon}k)` time for `s` points. (It is
assumed that no four of the points are cocircular.)
This sharpens the bound `O(sk ^ 2 \log s)` for Lee's
algorithm [Lee], and `O(s ^ 2 \log s + k(sk)
\log ^ 2 s)` for Chazelle and Edelsbrunner's algorithm
[ChE]. Finally, random sampling is used to show that
any set of `s` points in `E^3` has `O(sk^2\log ^ 8 s /
( \log \log s) ^ 6 )` distinct `j`sets with `j\leq k`.
(For `S \subset E^d`, a set `S'\subset S` with `S'=j`
is a `j`set of `S` if there is a halfspace `h^+` with
`S'=S \cap h^+`.) This sharpens with respect to `k` the
previous bound `O(s k ^ 5 )` [ChP]. The proof of
the bound given here is an instance of a "probabilistic
method" [ErS].
},
Annote = {
This paper extends the ideas of an earlier one
to a much more general setting; This framework is slightly less general
than that of range spaces of finite VCdimension, which were introduced
to geometric algorithms at the
same time by Haussler and Welzl (in the same
issue of the journal).
The scheme here is very close to the
sample compression
framework in the learning theory literature. It is not clear
that there are any natural applications of the VCdimension for which
this framework is not also applicable.
The framework is much the same as this
later one, which gives bounds that hold on
average; here the bounds hold with high probability, but with an extra factor
of `\log r`. The arrangement search structure uses a construction
that sharpens an idea of Megiddo, and in turn was later sharpened (removing
the `\epsilon`) and called cuttings. The polytope separation distance
algorithm was made obsolete by Gaertner, who gave an `O(n)`time algorithm
by application and extension of these ideas, and later improvements.
The `k`set bounds are sharpened here, with
a much cleaner argument. The construction used for the `k`set bounds,
considered in a dual arrangement of planes, is an approximate level, always
above an `n/r` level and always below a slightly higher level; that is,
it is a crude kind of "shallow cutting".
}}
@article{Guibas87,
Author = {L. J. Guibas and Jorge Stolfi and Kenneth L. Clarkson},
Journal = {Theoretical Computer Science},
Pages = {8184},
Title = {Solving Related Two and Three Dimensional Linear Programming Problems in Logarithmic Time},
Volume = {49},
Year = {1987}}
@inproceedings{mp,
Address = {New York, New York},
Author = {Kenneth L. Clarkson},
Booktitle = {STOC '87: } # proc # {Nineteenth } # stoc,
Month = {May},
Pdf = {mp/p.pdf},
Ps = {mp/p.ps},
Title = {Approximation Algorithms for Shortest Path Motion Planning},
Year = {1987},
Abstract = {
This paper gives approximation algorithms for solving
the following motion planning problem: Given a set of
polyhedral obstacles and points `s` and `t`, find a
shortest path from `s` to `t` that avoids the obstacles.
The paths found by the algorithms are piecewise linear,
and the length of a path is the sum of the lengths of the
line segments making up the path. Approximation algorithms
will be given for versions of this problem in the plane
and in threedimensional space. The algorithms return an
`\epsilon`short path, that is, a path with length within
`(1+\epsilon)` of shortest. Let `n` be the total number of
faces of the polyhedral obstacles, and `\epsilon` a given
value satisfying `0<\epsilon\leq\pi`. The algorithm
for the planar case requires `O(n\log n)/\epsilon`
time to build a data structure of size `O(n/\epsilon)`.
Given points `s` and `t`, an `\epsilon`short path from `s`
to `t` can be found with the use of the data structure
in time `O(n/\epsilon+n\log n)`. The data structure
is associated with a new variety of Voronoi diagram.
Given obstacles `S\subset E^3` and points `s,t\in E^3`,
an `\epsilon`short path between `s` and `t` can be found
in
$$O(n^2\lambda(n)\log(n/\epsilon)/\epsilon^4
+n^2\log n\rho\log(n\log \rho))$$
time, where `\rho` is the
ratio of the length of the longest obstacle edge
to the distance between `s` and `t`. The function
`\lambda(n)=\alpha(n)^{O(\alpha(n)^{O(1)})}`,
where the `\alpha(n)` is a form of inverse of
Ackermann's function. For `\log(1/\epsilon)` and
`\log\rho` that are `O(\log n)`, this bound is
`O(n^2(\log^2n)\lambda(n)/\epsilon^4)`.
},
Annote = {
This paper introduces the observation that Yao's fan
of cones can be used to build a spanner.
This observation was independently made by
Keil and Gutwin,
and by Ruppert and Seidel.
The resulting spanners have seen recent (circa 2005)
application in the wireless literature, where they are called
Yao graphs.
}}
@inproceedings{l1mp,
Address = {Waterloo, Ontario},
Author = {Kenneth L. Clarkson and S. Kapoor and P. Vaidya},
Booktitle = {SoCG '87: } # proc # {Third } # socg,
Month = {June},
Ps = {l1mp/p.ps},
Title = {Rectilinear Shortest Paths through Polygonal Obstacles in `{O(n \log^2 n)}` Time},
Year = {1987},
Abstract = {
The problem of finding a rectilinear shortest path
amongst obstacles may be stated as follows: Given a set
of obstacles in the plane find a shortest rectilinear
(`L_1`) path from a point `s` to a point `t` which
avoids all obstacles. The path may touch an obstacle
but may not cross an obstacle. We study the rectilinear
shortest path problem for the case where the obstacles
are nonintersecting simple polygons, and present an `O(
n \log^2 n )` algorithm for finding such a path,
where `n` is the number of vertices of the obstacles. This
algorithm requires `O(n \log n )` space. Another algorithm
is given that requires `O(n ( \log n ) ^{ 3/2} )` time and
space. We also study the case of rectilinear obstacles in
three dimensions, and show that `L_1` shortest paths
can be found in `O( n^2 \log^ 3 n )` time.
}}
@article{Clarkson89,
Author = {Kenneth L. Clarkson},
Journal = {Algorithmica},
Note = {Included in PhD Thesis},
Pages = {461469},
Title = {An Algorithm for Geometric Minimum Spanning Trees Requiring Nearly Linear Expected Time},
Url = {thesis_abs.html},
Volume = {4},
Year = {1989},
Abstract = {
We describe an algorithm for finding a minimum spanning
tree of the weighted complete graph induced by a set
of `n` points in Euclidean `d`space. The algorithm
requires nearly linear expected time for points that are
independently uniformly distributed in the unit `d`cube.
The first step of the algorithm is the spiral search
procedure described by Bentley, Weide, and Yao [BWY] for
finding a supergraph of the MST that has `O(n)` edges.
(The constant factor in the bound depends on `d`.) The next
step is that of sorting the edges of the supergraph by
weight using a radix distribution, or "bucket," sort.
These steps require linear expected time. Finally,
Kruskal's algorithm is used with the sorted edges,
requiring `O(n\alpha(cn,n))` time in the worst case,
with `c>6`. Since the function `\alpha(cn,n)` grows
very slowly, this step requires linear time for all
practical purposes. This result improves the previous best
`O(n\log\log^*n)`, and employs a much simpler algorithm.
Also, this result demonstrates the robustness of bucket
sorting, which requires `O(n)` expected time in this
case despite the probability dependency between the edge
weights.
},
BdskUrl1 = {thesis_abs.html}}
@inproceedings{Clarkson88,
Address = {Urbana, Illinois},
Author = {Kenneth L. Clarkson},
Booktitle = {SoCG '88: } # proc # {Fourth } # socg,
Month = {June},
Title = {Applications of Random Sampling in Computational Geometry, {II}},
Url = {#rs2m},
Year = {1988},
BdskUrl1 = {#rs2m}}
@inproceedings{Clarkson88a,
Address = {Urbana, Illinois},
Author = {Kenneth L. Clarkson and P. W. Shor},
Booktitle = {SoCG '88: } # proc # {Fourth } # socg,
Month = {June},
Title = {Algorithms for Diametral Pairs and Convex Hulls that are Optimal, Randomized, and Incremental},
Url = {#rs2m},
Year = {1988},
BdskUrl1 = {#rs2m}}
@article{rs2m,
Author = {Kenneth L. Clarkson and P. W. Shor},
Journal = dcg,
Note = {Merges two papers above},
Number = {1},
Pages = {387421},
Pdf = {rs2m/p.pdf},
Ps = {rs2m/p.ps},
Title = {Applications of Random Sampling in Computational Geometry, {II}},
Volume = {4},
Year = {1989},
Abstract = {
We use random sampling for several new geometric
algorithms. The algorithms are "Las Vegas," and their
expected bounds are with respect to the random behavior of
the algorithms. These algorithms follow from new general
results giving sharp bounds for the use of random subsets
in geometric algorithms. These bounds show that random
subsets can be used optimally for divideandconquer,
and also give bounds for a simple, general technique for
building geometric structures incrementally. One new
algorithm reports all the intersecting pairs of a set of
line segments in the plane, and requires `O(A+n\log n)`
expected time, where `A` is the number of intersecting
pairs reported. The algorithm requires `O(n)` space in
the worst case. Another algorithm computes the convex
hull of `n` points in `E^d` in `O(n\log n)` expected
time for `d=3`, and `O(n^{\lfloor d/2\rfloor})` expected time for `d>3`.
The algorithm also gives fast expected times for random
input points. Another algorithm computes the diameter
of a set of `n` points in `E^3` in `O(n\log n)` expected
time, and on the way computes the intersection of `n` unit
balls in `E^3`. We show that `O(n\log A)` expected time
suffices to compute the convex hull of `n` points in `E^3`,
where `A` is the number of input points on the surface of
the hull. Algorithms for halfspace range reporting are
also given. In addition, we give asymptotically tight
bounds for `(\le k)`sets, which are certain halfspace
partitions of point sets, and give a simple proof of Lee's
bounds for high order Voronoi diagrams.
}}
@article{Clarkson89b,
Appearedin = {SoCG '88: } # proc # {Fourth } # socg,
Author = {Kenneth L. Clarkson and Robert E. Tarjan and C. J. Van Wyk},
Ayear = {1988},
Journal = dcg,
Number = {1},
Pages = {423432},
Title = {A Fast {L}as {V}egas Algorithm for Triangulating a Simple Polygon},
Volume = {4},
Year = {1989},
Abstract = {
We present an algorithm that triangulates a simple
polygon on `n` vertices in `O(n \log ^* n)` expected
time. The algorithm uses random sampling on the
input, and its running time does not depend on any
assumptions about a probability distribution from
which the polygon is drawn.
},
Annote = {
The first algorithm to apply randomization to
the problem, and obtain essentially linear time.
See here also. Chazelle got
rid of the "essentially"
and the randomization, at the cost of some complexity;
Amato et al. took
out some of that complexity, at the cost of putting
the randomization back in.
}}
@article{lp2,
Appearedin = {FOCS '88: } # proc # {TwentyNinth } # focs,
Author = {Kenneth L. Clarkson},
Ayear = {1988},
Dvi = {lp/p.dvi},
Journal = jacm,
Number = {2},
Pages = {488499},
Pdf = {lp/p.pdf},
Ps = {lp/p.ps},
Slides = {lp/talk.pdf},
Title = {{L}as {V}egas algorithms for linear and integer programming when the dimension is small},
Volume = {42},
Year = {1995},
Abstract = {
This paper gives an algorithm for solving linear
programming problems. For a problem with `n` constraints
and `d` variables, the algorithm requires an expected
$$O(d^2n)+(\log n)O(d)^{d/2+O(1)}
+O(d^4\sqrt{n}\log n)$$
arithmetic operations, as `n\rightarrow\infty`.
The constant factors do not depend on `d`. Also, an
algorithm is given for integer linear programming.
Let `\varphi` bound the number of bits required to
specify the rational numbers defining an input constraint
or the objective function vector. Let `n` and `d`
be as before. Then the algorithm requires expected
$$O(2^ddn+8^dd\sqrt{n\ln n}\ln n)
+d^{O(d)}\varphi\ln n$$
operations on numbers with `d^{O(1)}\varphi` bits, as
`n>oo`, where the constant factors do not depend on
`d` or `\varphi`. The expectations are with respect to
the random choices made by the algorithms, and the bounds
hold for any given input. The technique can be extended
to other convex programming problems. For example, an
algorithm for finding the smallest sphere enclosing a set
of `n` points in `E^d` has the same time bound.
},
Annote = {
The bound given for integer programming is not quite right,
as corrected by F. Eisenbrand,
here.
(The accompanying talk is the one given at FOCS in 1988; note that it gives the results
of the paper in terms of the Smallest Enclosing Sphere (or Minimum Enclosing Ball) problem.)
The delay between conference and journal publication is not the fault of the journal.
Developments between 1988 and 1995, roughly as discussed at the conclusion of the journal version of the paper:
Several developments have occurred since the conference version
of this paper appeared.
Adler and Shamir have shown that these ideas can be applied to
general convex programming.
Chazelle and Matou{\v s}ek have derandomized the recursive algorithm,
obtaining a deterministic algorithm requiring
`d^{O(d)}n` time.
Alon and Megiddo have applied and extended the ideas
of this paper to a parallel setting.
Seidel gave a different randomized algorithm, requiring
`O(d!n)` expected time, with a somewhat simpler analysis;
Matousek, Sharir and Welzl found a variant of Seidel's algorithm
requiring time subexponential in `d`. Their algorithm is
a randomized instance of the simplex algorithm.
Kalai was the first to find a subexponential simplex
algorithm. Problem instances have long been known
for which versions of the simplex algorithm require at least `2^d`
operations.[KM] These results cast new light on the complexity
of the simplex algorithm, and on the possibility that linear programming
problems can be solved in "strongly polynomial" time; such an algorithm
would need
`(nd)^{O(1)}` operations, with the number of operations
independent of the size of the numbers specifying a problem instance.
Some more recent related results:
Vapnik's leaveoneout error estimate for
support vector machines is a version of Lemma 3.2, generalized to quadratic
programming. (Such deleted error estimates were
found in the sixties.)
Chazelle et al.
observe that one of these
algorithms can be the basis for sublinear geometric
algorithms; another paper
observes that the approach works well from the standpoint
of multipass algorithms. Lemma 3.2 (or its generalization
to convex programming), was
rediscovered recently:
Calafiore and Campi, Theorem 1.
Their proof rediscovers "backwards analysis".
}}
@article{BCL,
Appearedin = {SODA '90: } # proc # {First } # soda,
Author = {Jon L. Bentley and Kenneth L. Clarkson and David B. Levine},
Ayear = {1990},
Journal = {Algorithmica},
Pages = {168183},
Title = {Fast Linear ExpectedTime Algorithms for Computing Maxima and Convex Hulls},
Year = {1993},
Annote = {Related to this paper.}}
@article{Clarkson90,
Appearedin = {FOCS '88: } # proc # {TwenthNinth } # focs,
Author = {Kenneth L. Clarkson and Herbert Edelsbrunner and Leonidas J. Guibas and Micha Sharir and Emo Welzl},
Ayear = {1988},
Journal = dcg,
Number = {2},
Pages = {99160},
Title = {Combinatorial complexity bounds for arrangements of curves and surfaces},
Volume = {5},
Year = {1990}}
@inproceedings{tsp,
Author = {Kenneth L. Clarkson},
Booktitle = {SODA '91: } # proc # {Second } # soda,
Month = {January},
Pdf = {tsp/p.pdf},
Ps = {tsp/p.ps},
Title = {Approximation algorithms for planar traveling salesman tours and minimumlength triangulations},
Year = {1991},
Abstract = {
This paper gives a partitioning scheme for the geometric,
planar traveling salesman problem, under the Euclidean
metric: given a set `S` of `n` points in the plane, find
a shortest closed tour (path) visiting all the points.
The scheme employs randomization, and gives a tour
that can be expected to be short, if `S` satisfies
the condition that a random subset `R\subset S` has on
average a tour much shorter than an optimal tour of `S`.
This condition holds for points independently, identically
distributed in the plane, for example, for which a tour
within `1+\epsilon` of shortest can be found in expected
time `nk^2 2^k`, where `k=O(\log\log n)^3/\epsilon^2`.
One algorithm employed in the scheme is of interest
in its own right: when given a simple polygon `P`, it
finds a Steiner triangulation of the interior of `P`.
If `P` has `n` sides and perimeter `L_P`, the edges
of the triangulation have total length `L_PO(\log n)`.
If this algorithm is applied to a simple polygon induced
by a minimum spanning tree of a point set, the result is a
Steiner triangulation of the set with total length within
a factor of `O(\log n)` of the minimum possible.
},
Annote = {
A better partitioning scheme was given by Eppstein, using quadtrees,
and of course Arora's algorithm makes the whole approach moot.
There is a certain foreshadowing here of "pseudotriangulations",
however.
}}
@article{tri,
Appearedin = {SoCG '91: } # proc # {Seventh } # socg,
Author = {Kenneth L. Clarkson and Richard Cole and Robert E. Tarjan},
Ayear = {1991},
Slides = {tri/t.pdf},
Slidesnote1 = {n},
Slidesnote2 = {n},
Dvi = {tri/p.dvi},
Journal = {Int. J. Comp. Geom. and Applications},
Pages = {117133},
Pdf = {tri/p.pdf},
Ps = {tri/p.ps},
Title = {Randomized parallel algorithms for trapezoidal diagrams},
Year = {1992},
Abstract = {
We describe randomized parallel algorithms for building
trapezoidal diagrams of line segments in the plane.
The algorithms are designed for a CRCW PRAM. For general
segments, we give an algorithm requiring optimal `O(A+n\log
n)` expected work and optimal `O(\log n)` time, where
`A` is the number of intersecting pairs of segments.
If the segments form a simple chain, we give an algorithm
requiring optimal `O(n)` expected work and `O(\log
n\log\log n\log^*n)` expected time, and a
simpler algorithm requiring `O(n\log^*n)` expected work.
The serial algorithm corresponding to the latter is among
the simplest known algorithms requiring `O(n\log^*n)`
expected operations. For a set of segments forming `K`
chains, we give an algorithm requiring `O(A+n\log^*n+K\log n)`
expected work and `O(\log n\log\log n\log^* n)`
expected time. The parallel time bounds require the
assumption that enough processors are available, with
processor allocations every `\log n` steps.
},
Annote = {
The serial version of our basic algorithm, as applied to nonintersecting segments,
is a bit simpler
than the divideandconquer scheme of the earlier paper,
and not very far from Seidel's algorithm,
independently discovered at the same time as this one. (His algorithm uses planar
point location for a key task, while we use a sweepline algorithm.)
}}
@article{cms,
Appearedin = {Proc. Symp. Theor. Aspects of Comp. Sci.},
Author = {Kenneth L. Clarkson and Kurt Mehlhorn and Raimund Seidel},
Ayear = {1992},
Dvi = {cms/p.dvi},
Slides = {cms/t.pdf},
Slidesnote1 = {n},
Slidesnote2 = {n},
Journal = {Comp. Geom.: Theory and Applications},
Pages = {185121},
Pdf = {cms/p.pdf},
Ps = {cms/p.ps},
Title = {Four results on randomized incremental constructions},
Year = {1993},
Abstract = {
We prove four results on randomized incremental constructions (RICs):
 an analysis of the expected behavior under insertion and deletions,
 a fully dynamic data structure for convex hull maintenance in arbitrary dimensions,
 a tail estimate for the space complexity of RICs,
 a lower bound on the complexity of a game related to RICs.
},
Annote = {
Among other things, this paper extends Seidel's "backwards analysis" approach
(not far from the "leave one out" technique of learning theory) to a general
version of RIC; this involves a kind of "searching in history" and exploitation
of the exchangeability of members of a random sample.
The talk slides are missing a few figures.
}}
@incollection{rga,
Author = {Kenneth L. Clarkson},
Booktitle = {Computers and Euclidean Geometry},
Category = {Survey},
Editor = {F. K. Hwang and D. Z. Hu},
Pdf = {rga/p.pdf},
Ps = {rga/p.ps},
Publisher = {World Scientific Publishing},
Title = {Randomized Geometric Algorithms},
Year = {1992},
Abstract = {
This paper surveys some of the applications of randomization to computational
and combinatorial geometry. Randomization provides
a general way to divideandconquer geometric problems,
and gives a simple incremental approach to building geometric structures.
The paper discusses closestpoint problems,
convex hulls, Voronoi diagrams, trapezoidal diagrams of line segments,
linear programming in small dimension, range queries,
and bounds for pointline incidences and for `(\le k)`sets.
Relations to the
VapnikChervonenkis dimension, PAClearnability of geometric concepts,
and the Hough transform are briefly noted.
}}
@article{kmins,
Author = {Kenneth L. Clarkson},
Dvi = {kmins/p.dvi},
Journal = dcg,
Pages = {227233},
Pdf = {kmins/p.pdf},
Ps = {kmins/p.ps},
Title = {A bound on local minima of arrangements that implies the upper bound theorem},
Volume = {10},
Year = {1993},
Abstract = {
This paper shows that the `i`level of an arrangement of hyperplanes in
`E^d` has at most `\binom{i+d1}{d1}` local minima.
This bound follows from ideas previously used to prove bounds on `(\le k)`sets.
Using linear programming duality,
the Upper Bound Theorem is obtained as a corollary,
giving yet another proof of this
celebrated bound on the number of vertices of a simple polytope
in `E^d` with `n` facets.
},
Annote = {This paper by Mulmuley seems closely
related, and probably should have been cited.
}}
@inproceedings{dets,
Address = {Pittsburgh, PA},
Author = {Kenneth L. Clarkson},
Booktitle = {FOCS '92: } # proc # {ThirtyFirst } # focs,
Dvi = {dets/p.dvi},
Slides = {dets/t.pdf},
Slidesnote1 = {n},
Slidesnote2 = {n},
Month = {October},
Pages = {387395},
Pdf = {dets/p.pdf},
Ps = {dets/p.ps},
Title = {Safe and Effective Determinant Evaluation},
Year = {1992},
Abstract = {
The problem of evaluating the sign of the determinant of a
small matrix arises in many geometric algorithms.
Given an `n\times n` matrix `A` with integer entries, whose columns are all
smaller than `M` in Euclidean norm,
the algorithm given here evaluates the sign of the determinant `\det A`
exactly. The algorithm requires an arithmetic precision of less
than `1.5n+2\lg M` bits.
The number of arithmetic operations needed
is `O(n^3)+O(n^2)\log\OD(A)/\beta`,
where `\OD(A)\det A` is the product of the lengths of the columns of `A`,
and `\beta` is the number of "extra" bits of precision,
$$\min\{\lg(1/\mathbf{u})1.1n2\lg n2,\lg N  \lg M  1.5n  1\},$$
where `\mathbf{u}` is the roundoff error in approximate arithmetic, and `N`
is the largest representable integer.
Since `\OD(A)\leq M^n`, the algorithm requires `O(n^3\lg M)` time,
and `O(n^3)` time when `\beta=\Omega(\log M)`.
}}
@unpublished{hulltalk,
Author = {Kenneth L. Clarkson},
Nobib = {y},
Note = {Presentation at Fifth MSIStony Brook Workshop on Computational Geometry},
Ps = {hulltalk.ps},
Title = {Convex Hulls: Some Algorithms and Applications},
Url = {hulltalk.html},
Year = {1996},
Abstract = {
This talk sketches: a convenient method for
computing the volumes of Voronoi regions; a proof that
"areastealing" natural neighbor interpolation works;
a scheme for smoother natural neighbor interpolation
alternative to Sibson's method; the interpolation
scheme used in the "finite volume element" method;
and the observation that the minimax piecewiselinear
interpolant of a convex function is the (lower)
convex hull.
},
BdskUrl1 = {hulltalk.html}}
@article{center,
Appearedin = {SoCG '93: } # proc # {Ninth } # socg,
Author = {Kenneth L. Clarkson and David Eppstein and Gary L. Miller and Carl Sturtivant and ShangHua Teng},
Ayear = {1993},
Demo = {center/demo.html},
Demoscrolling = {auto},
Dvi = {center/p.dvi},
Journal = {International Journal of Computational Geometry and Applications},
Number = 3,
Pages = {357377},
Pdf = {center/p.pdf},
Ps = {center/p.ps},
Title = {Approximating center points with iterated {R}adon points},
Volume = 6,
Year = {1996},
Abstract = {
We give a practical and provably good Monte Carlo algorithm
for approximating center points. Let `P` be a set of `n`
points in `\reals^d`. A point `c \in \reals^d` is a `\beta`center
point of `P` if every closed halfspace containing `c`
contains at least `\beta n` points of `P`. Every point
set has a `1/(d+1)`center point; our algorithm finds
an `\Omega(1/d^2)`center point with high probability.
Our algorithm has a small constant factor and is the first
approximate center point algorithm whose complexity is
subexponential in `d`. Moreover, it can be optimally
parallelized to require `O(\log^2d\log\log n)` time.
Our algorithm has been used in mesh partitioning methods
and can be used in constructing high breakdown estimators
for multivariate datasets in statistics. It has the
potential to improve results in practice for constructing
weak `\epsilon`nets. We derive a variant of our algorithm
whose time bound is fully polynomial in `d` and linear in
`n`, and show how to combine our approach with previous
techniques to compute high quality center points more
quickly.
}}
@inproceedings{Clarkson93b,
Author = {Kenneth L. Clarkson},
Booktitle = {WADS '93: Proceedings of the Third Workshop on Algorithms and Data Structures},
Pages = {246252},
Title = {Algorithms for polytope covering and approximation},
Url = {#pcover},
Year = {1993},
BdskUrl1 = {#pcover}}
@inproceedings{Clarkson94,
Author = {Kenneth L. Clarkson},
Booktitle = {SoCG '94: } # proc # {Tenth } # socg,
Pages = {160164},
Title = {An algorithm for approximate closestpoint queries},
Url = {#pcover},
Year = {1994},
BdskUrl1 = {#pcover}}
@unpublished{pcover,
Author = {Kenneth L. Clarkson},
Dvi = {pcover/p.dvi},
Pdf = {pcover/p.pdf},
Ps = {pcover/p.ps},
Note = {Merges two papers above},
Title = {Algorithms for Polytope Covering and Approximation, and for Approximate Closestpoint Queries},
Year = {1994},
Abstract = {
This paper gives an algorithm for polytope covering:
let `L` and `U` be sets of points in `R^d`,
comprising `n` points altogether. A cover for
`L` from `U` is a set `C\subset U` with `L` a subset
of the convex hull of `C`. Suppose `c` is the size of
a smallest such cover, if it exists. The randomized
algorithm given here finds a cover of size no more than
`c(5d\ln c)`, for `c` large enough. The algorithm
requires `O(c^2n^{1+\delta})` expected time. (In
this paper, `\delta` will denote any fixed value
greater than zero.) More exactly, the time bound is
$$O(cn^{1+\delta}+c(nc)^{
1/(1+\gamma/(1+\delta))}),$$
where `\gamma\equiv 1/\lfloor d/2\rfloor`. The previous best bounds
were `c O(\log n)` cover size in `O(n^d)` time [MiS].
A variant algorithm is applied to the problem of
approximating the boundary of a polytope with the
boundary of a simpler polytope. For an appropriate
measure, an approximation with error `\epsilon`
requires `c=O(1/\epsilon)^{(d1)/2}` vertices, and the
algorithm gives an approximation with `c({5d^3\ln(1/\epsilon)})`
vertices. The algorithms apply ideas previously used
for smalldimensional linear programming. The final
result here applies polytope approximation to the the
post office problem: given `n` points (called
sites) in `d` dimensions, build a data structure so
that given a query point `q`, the closest site to `q`
can be found quickly. The algorithm given here is given
also a relative error bound `\epsilon`, and depends on
a ratio `\rho`, which is no more than the ratio of the
distance between the farthest pair of sites to the distance
between the closest pair of sites. The algorithm builds
a data structure of size `O(n(\log\rho)/\epsilon^{d/2}`
in time `O(n^2(\log\rho))/\epsilon^d`. With this data
structure, closestpoint queries can be answered in
`O(\log n)/\epsilon^{d/2}` time.
},
Annote = {
Brönnimann and Goodrich show that
the same iterative randomized algorithm
applies in the general setting of range spaces of bounded
VCdimension, and that
a linearsized `\epsilon`net implies a
constantfactor approximation algorithm. Their results
are extended by this later paper.
}}
@inproceedings{os,
Author = {Kenneth L. Clarkson},
Booktitle = {FOCS '94: } # proc # {ThirtyFifth } # focs,
Dvi = {os/p.dvi},
Pages = {695702},
Slides = {os/t.pdf},
Slidesnote1 = {n},
Slidesnote2 = {n},
Pdf = {os/p.pdf},
Ps = {os/p.ps},
Title = {More outputsensitive geometric algorithms},
Year = 1994,
Abstract = {
A simple idea for speeding up the computation of extrema
of a partially ordered set turns out to have a number
of interesting applications in geometric algorithms; the
resulting algorithms generally replace an appearance of the
input size `n` in the running time by an output size `A\leq
n`. In particular, the `A` coordinatewise minima of a set
of `n` points in `R^d` can be found by an algorithm needing
`O(nA)` time. Given `n` points uniformly distributed
in the unit square, the algorithm needs `n+O(n^{5/8})`
point comparisons on average. Given a set of `n` points
in `R^d`, another algorithm can find its `A` extreme
points in `O(nA)` time. Thinning for nearestneighbor
classification can be done in time `O(n\log n)\sum_i A_i n_i`,
finding the `A_i` irredundant points among `n_i`
points for each class `i`, where `n=\sum_i n_i` is the
total number of input points. This sharpens a more
obvious `O(n^3)` algorithm, which is also given here.
Another algorithm is given that needs `O(n)` space to
compute the convex hull of `n` points in `O(nA)` time.
Finally, a new randomized algorithm finds the convex hull
of `n` points in `O(n\log A)` expected time, under the
condition that a random subset of the points of size `r`
has expected hull complexity `O(r)`. All but the last
of these algorithms has polynomial dependence on the
dimension `d`, except possibly for linear programming.
},
Annote = {
There is some overlap with the work of
Chan and
Ottman et al,
in particular, for finding extreme points.
}}
@unpublished{moving_diam,
Author = {Kenneth L. Clarkson},
Dvi = {moving_diam/p.dvi},
Pdf = {moving_diam/p.pdf},
Ps = {moving_diam/p.ps},
Title = {Algorithms for the Minimum Diameter of Moving Points and for the Discrete 1center Problem},
Year = {1997},
Abstract = {
Given points moving with constant, but possibly different,
velocities, the minimum moving diameter problem is to
find the minimum, over all time, of the maximum distance
between a pair of points at each moment. This note gives
a randomized algorithm requiring `O(n \log n )` expected
time for this problem, in two and three dimensions.
Also briefly noted is a randomized `O(n\log n\log\log n)`
expectedtime algorithm for the discrete 1center problem
in three dimensions; in this problem, a member `p` of a
set `S` of points is desired, whose maximum distance to `S`
is minimum over all points of `S`.
},
Annote = {
Slight foreshadowing of sublinear geometric algorithms.
}}
@unpublished{recon,
Author = {Kenneth L. Clarkson},
Dvi = {recon/p.dvi},
Pdf = {recon/p.pdf},
Ps = {recon/p.ps},
Title = {A General Randomized Incremental Reconstruction Procedure},
Year = {1997},
Abstract = {
The technique of randomized incremental construction
allows a variety of geometric structures to be built
quickly. This note shows that once such a structure
is built, it is possible to store the geometric
input data for it so that the structure can be built
again by a randomized algorithm even more quickly.
Except for the randomization, this generalizes the
technique of Snoeyink and van Kreveld that applies to
planar problems.
}}
@unpublished{CIS677,
Author = {Kenneth L. Clarkson},
Note = {Lecture notes},
Title = {A graduate course in computational geometry},
Url = {cis677},
Year = {1997},
BdskUrl1 = {cis677}}
@article{nnms,
Appearedin = {STOC '97: } # proc # {TwentyNinth } # stoc,
Author = {Kenneth L. Clarkson},
Ayear = {1997},
Journal = dcg,
Pages = {6393},
Pdf = {nnms/p.pdf},
Ps = {nnms/p.ps},
Title = {Nearest Neighbor Queries in Metric Spaces},
Volume = 22,
Year = 1999,
Abstract = {
Given a set `S` of `n` sites (points), and a distance measure `d`,
the nearest neighbor searching problem
is to build a data structure so that given a query point `q`,
the site nearest to `q` can be found quickly.
This paper gives data structures for this problem when the sites and queries
are in a metric space.
One data structure, `D(S)`, uses a divideandconquer recursion.
The other data structure, `M(S,Q)`, is somewhat like a skiplist.
Both are simple and implementable.
The data structures are analyzed when the metric space obeys a
certain spherepacking bound, and when the sites and query points
are random and have distributions with an exchangeability property.
This property implies, for example, that
query point `q` is a random element of `S\cup\{q\}`.
Under these conditions,
the preprocessing and space bounds for the algorithms are close
to linear in `n`. They depend also on the spherepacking
bound, and on the logarithm of the distance ratio `\Upsilon(S)` of `S`,
the ratio of the distance between the farthest pair of
points in `S` to the distance between the closest pair.
The data structure `M(S,Q)`
requires as input data an additional
set `Q`, taken to be representative of the query points.
The resource bounds of `M(S,Q)` have a dependence on
the distance ratio of `S\cup Q`.
While `M(S,Q)` can return wrong
answers, its failure probability can be bounded, and
is decreasing in a parameter `K`. Here `K\leq Q/n` is chosen
when building `M(S,Q)`.
The expected query time for `M(S,Q)` is `O(K\log n)\log\Upsilon(S\cup Q)`,
and the resource bounds increase linearly in `K`.
The data structure `D(S)` has expected `O(\log n)^{O(1)}` query
time, for fixed distance ratio.
The preprocessing algorithm for `M(S,Q)` can be used to solve
the allnearestneighbor problem for `S` in
`O(n(\log n)^2(\log\Upsilon(S))^2)` expected time.
},
Annote = {
The assumption of a spherepacking bound is equivalent
to a bounded doubling constant, as discussed by
Krauthgamer and Lee,
which is more general than the growthrestricted
or doubling measure
assumption of Karger and Ruhl.
See also HarPeled and Mendel,
and a survey.
}}
@article{CSW,
Author = {Kenneth L. Clarkson and Wim Sweldens and Alice Zheng},
Journal = {IEEE Trans. Commun.},
Number = 2,
Pages = {253261},
Title = {Fast Multiple Antenna Differential Decoding},
Url = {http://cm.belllabs.com/who/wim/papers/fast/},
Volume = 49,
Year = {2001},
Abstract = {
We present an algorithm based on lattice reduction for the
fast decoding of diagonal differential modulation across
multiple antenna. While the complexity of the maximum
likelihood algorithm is exponential both in the number of
antenna and the rate, the complexity of our approximate
lattice algorithm is polynomial in the number of antennas
and the rate. We show that the error performance of our
lattice algorithm is very close to the maximum likelihood
algorithm.
},
BdskUrl1 = {http://cm.belllabs.com/who/wim/papers/fast/}}
@unpublished{coresets1,
Author = {Mihai Badoiu and Kenneth L. Clarkson},
Demo = {core_sets/t/demo.svg},
Note = {Manuscript},
Pdf = {coresets1.pdf},
Slides = {core_sets/t/t.xml},
Slidesnote2 = {y},
Title = {Optimal CoreSets for Balls},
Versions = {  Revised May 2006: Removed dependence on size of smallest ball, consider variant, etc.
},
Year = {2002},
Abstract = {
Given a set of points `P\subset \reals^d` and value `\epsilon>0`,
an $\epsilon$coreset `S \subset P` has the property that the smallest ball
containing `S` is within `\epsilon` of the smallest ball containing
`P`.
This paper shows that any point set has an `\epsilon`coreset
of size `\lceil 1/\epsilon\rceil `, and this bound is tight
in the worst case. Some experimental results are also
given, comparing this algorithm with a previous one,
and with a more powerful, but slower one.
}}
@inproceedings{coresets2,
Author = {Mihai Badoiu and Kenneth L. Clarkson},
Booktitle = {SODA '03: } # proc # {Fourteenth } # soda,
Pdf = {coresets2.pdf},
Title = {Smaller CoreSets for Balls},
Year = {2003},
Abstract = {
Given a set of points `P\subset R^d` and value `\epsilon>0`,
an `\epsilon`coreset `S \subset P` has the property that the smallest ball
containing `S` is within `\epsilon` of the smallest ball containing
`P`.
This paper shows that any point set has an `\epsilon`coreset
of size `\lceil 1/\epsilon\rceil `, and this bound is tight
in the worst case.
A faster algorithm given here finds an `\epsilon`coreset of size
at most `2/\epsilon`.
These results imply the existence of small coresets for solving approximate
`k`center
clustering and related problems. The sizes of these coresets
are considerably smaller than the previously known bounds,
and imply
faster algorithms; one such algorithm needs
`O(d n/\epsilon+(1/\epsilon)^{5})`
time to compute an `\epsilon`approximate
minimum enclosing ball (1center) of `n` points in `d`
dimensions.
A simple gradientdescent algorithm is also given, for computing
the minimum enclosing ball in `O(d n / \epsilon^{2})` time.
This algorithm also implies slightly faster algorithms for
computing approximately the smallest radius `k`flat fitting
a set of points.
},
Annote = {The ideas and algorithm have seen application in
machine learning, including support vector regression,
and computational biology (mentioned in the "supplementary notes" of the latter).}}
@unpublished{CH2,
Author = {Kenneth L. Clarkson and John D. Hobby},
Pdf = {covprob/p.pdf},
Ps = {covprob/p.ps},
Title = {A Model of Coverage Probability under Shadow Fading},
Year = {2003},
Abstract = {
We give a simple analytic model of coverage probability
for CDMA cellular phone systems under lognormally
distributed shadow fading. Prior analyses have generally
considered the coverage probability of a single antenna;
here we consider the probability of coverage by an
ensemble of antennas, using some independence assumptions,
but also modeling a limited form of dependency among the
antenna fades. We use the FentonWilkinson approach
of approximating the external interference `I_0` as
lognormally distributed. We show that our model gives
a coverage probability that is generally within a few
percent of Monte Carlo estimates, over a wide regime of
antenna strengths and other relevant parameters.
}}
@inproceedings{HCHP,
Author = {K. Georg Hampel and Kenneth L. Clarkson and John D. Hobby and Paul A. Polakos},
Booktitle = {VTC2003Fall: IEEE Vehicular Technology Conference},
Pages = {927932},
Pdf = {vtc2003.pdf},
Title = {The Tradeoff between Coverage and Capacity in Dynamic Optimization of 3{G} Cellular Networks},
Year = {2003},
Abstract = {
For 3G cellular networks, capacity is an important
objective, along with coverage, when characterizing the
performance of highdatarate services. In live networks,
the effective network capacity heavily depends on the
degree that the traffic load is balanced over all cells,
so changing traffic patterns demand dynamic network
reconfiguration to maintain good performance. Using a
fourcell sample network, and antenna tilt, cell power
level and pilot fraction as adjustment variables, we
study the competitive character of network coverage and
capacity in such a network optimization process, and
how it compares to the CDMAintrinsic coveragecapacity
tradeoff driven by interference. We find that each set of
variables provides its distinct coveragecapacity tradeoff
behavior with widely varying and applicationdependent
performance gains. The study shows that the impact of
dynamic load balancing highly depends on the choice of the
tuning variable as well as the particular tradeoff range
of operation.
}}
@article{BCGVW,
Author = {Simon C. Borst and Kenneth Clarkson and John Graybeal and Harish Viswanathan and Phillip Whiting},
Journal = {Bell Labs Technical Journal},
Number = 2,
Pages = {3347},
Pdf = {BCGVW.pdf},
Title = {UserLevel {QoS} and Traffic Engineering for {3G} Wireless {1xEVDO} Systems},
Volume = 8,
Year = {2003},
Abstract = {
3G wireless systems such as 3G1X, 1xEVDO and 1xEVDV
provide support for a variety of highspeed data
applications. The success of these services critically
relies on the capability to ensure an adequate QoS
experience to users at an affordable price. With
wireless bandwidth at a premium, traffic engineering and
network planning play a vital role in addressing these
challenges. We present models and techniques that we have
developed for quantifying the QoS perception of 1xEVDO
users generating FTP or Web browsing sessions. We show
how userlevel QoS measures may be evaluated by means of
a ProcessorSharing model which explicitly accounts for
the throughput gains from multiuser scheduling. The model
provides simple analytical formulas for key performance
metrics such as response times, blocking probabilities and
throughput. Analytical models are especially useful for
network deployment and inservice tuning purposes due to
the intrinsic difficulties associated with simulationbased
optimization approaches. We discuss the application of
our results in the context of Ocelot, which is a Lucent
tool for wireless network planning and optimization.
}}
@unpublished{MV,
Author = {Kenneth L. Clarkson},
Pdf = {mv.pdf},
Title = {Solution of Linear Systems Using Randomized Rounding},
Year = {2003},
Abstract = {
This paper gives an algorithm for solving linear systems, using a
randomized version of incomplete `LU` factorization together with
iterative improvement. The factorization uses Gaussian elimination
with partial pivoting, and preserves sparsity during factorization by
randomized rounding of the entries. The resulting approximate factorization
is then applied to estimate the solution. This simple technique,
combined with
iterative improvement, is demonstrated to be effective for a range of
linear systems. When applied to mediumsized sample matrices for PDEs,
the algorithm is qualitatively like multigrid: the work per iteration
is typically linear in the order of the matrix, and the number of
iterations to achieve a small residual is typically on the order of
fifteen to twenty. The technique is also tested for a sample of asymmetric
matrices from the Matrix Market, and is found to have
similar behavior for many of them.
}}
@unpublished{SB,
Author = {Kenneth L. Clarkson},
Demo = {sb/t/demo.svg},
Note = {Preliminary version presented at ALENEX99},
Slides = {sb/t/vv.xml},
Slidesnote2 = {y},
Title = {Nearest Neighbor Searching in Metric Spaces: Experimental Results for `sb(S)`},
Url = {Msb/readme.html},
Year = 2003,
Abstract = {
Given a set `S` of `n` sites (points), and a distance
measure `d`, the nearest neighbor searching problem is to build
a data structure so that given a query point `q`, the site nearest
to `q` can be found quickly. This paper gives a data structure for
this problem; the data structure is built using the distance function
as a "black box". The structure is able to speed up nearest
neighbor searching in a variety of settings, for example: points
in lowdimensional or
structured Euclidean space, strings under Hamming and edit distance,
and bit vector data from an OCR application. The data structures are
observed to need linear space, with a modest constant factor. The
preprocessing time needed per site is observed to match the
query time. The data structure can
be viewed as an application of a "kdtree" approach in the metric
space setting, using Voronoi regions of a subset in place of
axisaligned boxes.
},
BdskUrl1 = {Msb/readme.html}}
@inproceedings{dynamics,
Author = {Kenneth L. Clarkson and John D. Hobby},
Booktitle = {VTC2004Spring: IEEE Vehicular Technology Conference},
Demo = {dynamics/demo.svg},
Pages = {15341538},
Pdf = {dynamics/p.pdf},
Ps = {dynamics/p.ps},
Slides = {dynamics/t.xml},
Slidesnote2 = {y},
Title = {A Model of Soft Handoff under Dynamic Shadow Fading},
Volume = 3,
Year = {2004},
Abstract = {
We introduce a simple model of the effect of temporal
variation in signal strength on activeset membership,
for cellular phone systems that use the softhandoff
algorithm of IS95a. This model is based on a
steadystate calculation, and its applicability is
confirmed by Monte Carlo studies.
}}
@unpublished{cwi_ksets,
Author = {Kenneth L. Clarkson},
Dvi = {cwi_ksets/p.dvi},
Pdf = {cwi_ksets/p.pdf},
Ps = {cwi_ksets/p.ps},
Title = {On the Expected Number of `k`sets of CoordinateWise Independent Points},
Year = {2004},
Abstract = {
Let `S` be a set of `n` points in `d` dimensions.
A `k`set of `S` is a subset of size `k` that is
the intersection of `S` with some open halfspace.
This note shows that if the points of `S` are random,
with a coordinatewise independent distribution,
then the expected number of `k`sets of `S` is
`O((k\log(en/k))^{d1})2^d/(d1)!`, as `k\log n>\infty`,
with a constant independent of the dimension.
}}
@inproceedings{l1,
Author = {Kenneth L. Clarkson},
Booktitle = {SODA '05: } # proc # {Sixteenth } # soda,
Dvi = {l1/p.dvi},
Pdf = {l1/p.pdf},
Ps = {l1/p.ps},
Slides = {l1/t/t.xml},
Slidesnote1 = {y},
Slidesnote2 = {y},
Title = {Subgradient and Sampling Algorithms for `L_1` Regression},
Year = {2005},
Abstract = {
Given an `n\times d` matrix `A`
and an `n`vector `b`, the `L_1`
regression problem is to find the
vector `x` minimizing the objective
function `Axb_1`, where
`y_1 \equiv \sum_i y_i` for vector
`y`. This paper gives an algorithm needing
`O(n\log n)d^{O(1)}` time in the worst case
to obtain an approximate solution, with
objective function value within a fixed
ratio of optimum. Given `\epsilon>0`, a
solution whose value is within `1+\epsilon`
of optimum can be obtained either
by a deterministic algorithm using an
additional `O(n)(d/\epsilon)^{O(1)}` time,
or by a Monte Carlo algorithm using an
additional `O((d/\epsilon)^{O(1)})` time.
The analysis of the randomized algorithm
shows that weighted coresets exist for
`L_1` regression. The algorithms use the
ellipsoid method, gradient descent, and
random sampling.
}}
@article{set_cover,
Appearedin = {SoCG '05: } # proc # {TwentyFirst } # socg,
Author = {Kenneth L. Clarkson and Kasturi Varadarajan},
Ayear = {2005},
Journal = dcg,
Month = {January},
Number = {1},
Pages = {4358},
Pdf = {set_cover/p.pdf},
Ps = {set_cover/p.ps},
Slides = {set_cover/t/t.xml},
Slidesnote1 = {y},
Title = {Improved Approximation Algorithms for Geometric Set Cover},
Volume = {37},
Year = {2007},
Abstract = {
Given a collection `S` of subsets of some set `U`, and
`M \subset U`, the set cover problem is to find
the smallest subcollection `C\subset S` such that `M` is
a subset of the union of the sets in `C`. While the general
problem is NPhard to solve, even approximately, here we
consider some geometric special cases, where usually
`U = \reals^d`. Combining previously known techniques [BG,CF], we show
that polynomial time approximation algorithms with provable performance exist,
under a certain general condition: that for a random subset
`R\subset S` and function `f()`, there is a decomposition
of the complement `U\setminus\cup_{Y\in R} Y` into an
expected `f(R)` regions, each region of a particular
simple form. Under this condition, a cover
of size `O(f(C))` can be found in polynomial time. Using this result,
and combinatorial geometry results implying
bounding functions `f(c)` that are nearly linear,
we obtain `o(\log c)` approximation algorithms
for covering by fat triangles, by pseudodisks,
by a family of fat objects,
and others. Similarly, constantfactor
approximations follow for similarsized fat triangles
and fat objects, and for fat wedges.
With more work, we obtain constantfactor
approximation algorithms for covering by unit cubes in
`\reals^3`, and for guarding an `x`monotone polygonal chain.
},
Annote = {
While no great claim of novelty was made for Theorem 2.2,
it should be pointed out that the proof is very close to that by
de Berg and Cheong (n Schwarzkopf).
(The related proof appears only in the journal version of their paper.
Thanks to Sariel HarPeled for pointing this out.)
Moreover, recent work (circa 2008) tightens some of the
nonlinear cases;
see also here.
}}
@incollection{nn_survey,
Author = {Kenneth L. Clarkson},
Booktitle = {NearestNeighbor Methods for Learning and Vision: Theory and Practice},
Category = {Survey},
Dvi = {nn_survey/p.dvi},
Editor = {Gregory Shakhnarovich and Trevor Darrell and Piotr Indyk},
Isbn = {026219547X},
Pages = {1559},
Pdf = {nn_survey/p.pdf},
Ps = {nn_survey/p.ps},
Publisher = {MIT Press},
Slides = {nn_survey/t/t.xml},
Slidesnote1 = {y},
Title = {NearestNeighbor Searching and Metric Space Dimensions},
Year = {2006},
Abstract = {
Given a set `S` of points in a metric
space with distance function `D`, the
nearestneighbor searching problem is to build a
data structure for `S` so that for an input query point `q`,
the point `s\in S` that minimizes `D(s,q)`
can be found quickly.
We survey approaches to this problem, and its
relation to concepts of metric space dimension.
Several measures of dimension can be estimated
using nearestneighbor searching, while others
can be used to estimate the cost of that searching.
In recent years, several data structures have been
proposed that are provably good for lowdimensional
spaces, for some particular measures of dimension.
These and other data structures for nearestneighbor
searching are surveyed.
},
Annote = {
Some dimensions discussed: box, packing, Hausdorff, Assouad,
pointwise, information, correlation, quantization, energy,
Renyi.
}}
@inproceedings{enet_tris,
Author = {Kenneth L. Clarkson},
Booktitle = {STOC '06: } # proc # {ThirtyEighth } # stoc,
Demo = {graded/demo.xml},
Dvi = {enet_tris/p.dvi},
Pdf = {enet_tris/p.pdf},
Ps = {enet_tris/p.ps},
Slides = {enet_tris/t/t.xml},
Slidesnote1 = {y},
Title = {Building Triangulations Using `\epsilon`Nets},
Versions = {  Revised from 20/2/06: patches to upper bound proof, lower bound proof, many typos etc.
 Revised from version of 11/19/05: better upper bound proof, typos in Dudley example, cites peyre/cohen.
},
Year = {2006},
Abstract = {
This work addresses the problem of approximating
a manifold by a simplicial mesh, and the related
problem of building triangulations for the purpose of
piecewiselinear approximation of functions. It has
long been understood that the vertices of such meshes
or triangulations should be "welldistributed," or
satisfy certain "sampling conditions." This work
clarifies and extends some algorithms for finding such
welldistributed vertices, by showing that they can
be regarded as finding `\epsilon`nets or
Delone sets in appropriate metric spaces. In some cases
where such Delone properties were already understood,
such as for meshes to approximate smooth manifolds that
bound convex bodies, the upper and lower bound results
are extended to more general manifolds; in particular,
under some natural conditions, the minimum Hausdorff
distance for a mesh with `n` simplices to a `d`manifold
`M` is
$$\Theta((\int_M\sqrt{\kappa(x)}/n)^{2/d})$$
as `n\rightarrow\infty`, where `\kappa(x)` is the
Gaussian curvature at point `x\in M`. We also relate
these constructions to Dudley's approximation scheme for
convex bodies, which can be interpreted as involving an
`\epsilon`net in a metric space whose distance function
depends on surface normals. Finally, a novel scheme is
given, based on the Steinhaus transform, for scaling
a metric space by a Lipschitz function to obtain a
new metric. This scheme is applied to show that some
algorithms for building finite element meshes and for
surface reconstruction can be also be interpreted in the
framework of metric space `\epsilon`nets.
}}
@inproceedings{rev_interference,
Author = {Kenneth L. Clarkson and K. Georg Hampel and John D. Hobby},
Booktitle = {VTC2007Fall: IEEE Vehicular Technology Conference},
Dvi = {rev_interference/p.dvi},
Note = {to appear, IEEE Transactions on Wireless Communications},
Pdf = {rev_interference/p.pdf},
Ps = {rev_interference/p.ps},
Title = {Modeling UpLink Power Control with Outage Probabilities},
Year = {2007},
Abstract = {
We investigate models for uplink interference in wireless systems.
Our models account for the effects of outage probabilities.
Such an accounting requires a nonlinear, even nonconvex model,
since increasing interference
at the receiving base station increases both mobile transmit power
and outage probability, and this results in a complex interaction.
Our system model always has at least one solution, a fixed point,
and it is provably unique under certain reasonable conditions. Our main
purpose is to model real wireless systems as accurately as possible,
and so we test our models on realistic scenarios using data from a
sophisticated simulator. Our algorithm for finding a fixed point works
very well on such scenarios, and is guaranteed to find the fixed point
when we can prove it is unique. A slightly simplified model reduces the
main data structure for a $K$sector market to $16K^2$ bytes of memory.
}}
@inproceedings{loss_model,
Author = {Kenneth L. Clarkson and John D. Hobby},
Booktitle = {VTC2007Fall: IEEE Vehicular Technology Conference},
Dvi = {loss_model/p.dvi},
Note = {Winner, conference Best Paper award.},
Pdf = {loss_model/p.pdf},
Ps = {loss_model/p.ps},
Title = {Ocelot's Knapsack Calculations for Modeling Power Amplifier and {W}alsh Code Limits},
Year = {2007},
Abstract = {
We give a model for the performance impact on wireless systems of
the limitations of certain resources, namely, the basestation power
amplifier and the available OVSF codes. These limitations are
readily modeled in the loss model formulation as a
stochastic knapsack. A simple and wellknown recurrence of
Kaufman and Roberts allows the predictions of the model to be
efficiently calculated. We discuss the assumptions and
approximations we have made that allow the use of the model. We have
included the model in Ocelot, a Lucent tool for modeling and
optimizing cellular phone systems. The model is fast to compute,
differentiable with respect to the relevant parameters, and able to
model broad ranges of capacity and resource use. These conditions
are critical to our application of optimization.
}}
@article{sga,
Appearedin = {SODA '08: } # proc # {Nineteenth } # soda,
Author = {Kenneth L. Clarkson},
Ayear = {2008},
Demo = {sga/t/demo/demo.svg},
Dvi = {sga/p.dvi},
Journal = talg,
Number = 4,
Pdf = {sga/p.pdf},
Ps = {sga/p.ps},
Slides = {sga/t/t.xml},
Slidesnote1 = {y},
Slidesnote2 = {y},
Title = {Coresets, Sparse Greedy Approximation, and the {F}rank{W}olfe Algorithm},
Versions = {  July 22, 2008: Various corrections (actually done October 2007)
 June 19, 2007: Corrected probabilistic existence proof (per K. Varadarajan), typos
 April 29, 2007: Clearer statement of results, cleaner proof for algorithms with away steps, typos
 March 26, 2007: First version
},
Volume = 6,
Year = {2010},
Abstract = {
The problem of maximizing a concave function $f(x)$ in a simplex $S$ can be
solved approximately by a simple greedy algorithm, that for given $k$ can
find a point $x_{(k)}$ on a $k$dimensional face such that $f(x_{(k)}) \ge
f(x_*)  O(1/k)$, where $f(x_*)$ is the maximum value of $f$ in $S$. This
algorithm and analysis were known before, and related to problems of
statistics and machine learning, such as boosting, regression, and density
mixture estimation. In other work, coming from computational geometry, the
existence of $\epsilon$coresets was shown for the minimum
enclosing ball problem, by means of a simple greedy algorithm. Similar
greedy algorithms, that are special cases of the FrankWolfe algorithm, were
described for other enclosure problems. Here these results are tied
together, stronger convergence results are reviewed, and several coreset
bounds are generalized or strengthened.
}}
@inproceedings{jl_manifold,
Author = {Kenneth L. Clarkson},
Booktitle = {SoCG '08: } # proc # {TwentyFourth } # socg,
Dvi = {jl_manifold/p.dvi},
Pdf = {jl_manifold/p.pdf},
Ps = {jl_manifold/p.ps},
Slides = {jl_manifold/t2/t.xml},
Slidesnote1 = {y},
Slidesnote2 = {y},
Title = {Tighter Bounds for Random Projections of Manifolds},
Versions = {  July 22, 2008: Various corrections (actually done March 2008)
 December 2007: First version
},
Year = {2008},
Abstract = {
The JohnsonLindenstrauss random projection lemma gives a simple way to
reduce the dimensionality of a set of points while approximately preserving
their pairwise distances. The most direct application of the lemma applies
to a finite set of points, but recent work has extended the technique to
affine subspaces, curves, and general smooth manifolds. Here the case of
random projection of smooth manifolds is considered, and a previous analysis
is sharpened, reducing the dependence on such properties as the manifold's
maximum curvature.}}
@inproceedings{self_improve,
Author = {Kenneth L. Clarkson and C. Seshadhri},
Booktitle = {SoCG '08: } # proc # {TwentyFourth } # socg,
Dvi = {self_improve/p.dvi},
Pdf = {self_improve/p.pdf},
Ps = {self_improve/p.ps},
Slidesnote1 = {n},
Slidesnote2 = {n},
Title = {SelfImproving Algorithms for {D}elaunay Triangulations},
Year = {2008},
Abstract = {
We study the problem of twodimensional Delaunay triangulation in the
selfimproving algorithms model. We assume that the `n`points of the input
each come from an independent, unknown, and arbitrary distribution. The
first phase of our algorithm builds data structures that store relevant
information about the input distribution. The second phase uses these data
structures to efficiently compute the Delaunay triangulation of the input.
The running time of our algorithm matches the informationtheoretic lower
bound for the given input distribution, implying that if the input
distribution has low entropy, then our algorithm beats the standard
`\Omega(n \log n)` bound for computing Delaunay triangulations.
Our algorithm and analysis use a variety of techniques: `\epsilon`nets for
disks, entropyoptimal pointlocation data structures, lineartime splitting
of Delaunay triangulations, and informationtheoretic arguments. }}
@article{self_improve2,
Appearedas = {SelfImproving Algorithms for {D}elaunay Triangulations},
Appearedin = {SoCG '08: } # proc # {TwentyFourth } # socg,
Author = {Nir Ailon and Bernard Chazelle and Kenneth L. Clarkson and Ding Liu and Wolfgang Mulzer and C. Seshadhri},
Ayear = 2008,
Slides = {self_improve/t/t.xml},
Slidesnote1 = {n},
Slidesnote2 = {n},
Journal = sicomp,
Note = {The prior paper on Delaunay triangulations with C. Seshadhri was combined with the paper by the other authors that introduced the model.},
Number = 2,
Title = {SelfImproving Algorithms},
Url = {http://arxiv.org/abs/0907.0884},
Volume = 40,
Year = 2011,
Abstract = {
We investigate ways in which an algorithm can improve its expected performance
by finetuning itself automatically with respect to an unknown input
distribution $D$. We assume here that $D$ is of product type. More precisely,
suppose that we need to process a sequence $I_1, I_2,\ldots$ of inputs
$ I = (x_1, x_2, \ldots, x_n)$ of some fixed length $n$, where each $x_i$ is drawn independently
from some arbitrary, unknown distribution $D_i$. The goal is to design an
algorithm for these inputs so that eventually the expected running time will
be optimal for the input distribution $D = D_1 * D_2 * ... * D_n$.
We give such selfimproving algorithms for two problems: (i) sorting a sequence
of numbers and (ii) computing the Delaunay triangulation of a planar point set.
Both algorithms achieve optimal expected limiting complexity. The algorithms
begin with a training phase during which they collect information about the
input distribution, followed by a stationary regime in which the algorithms
settle to their optimized incarnations.
},
BdskUrl1 = {http://arxiv.org/abs/0907.0884}}
@unpublished{socg_talk,
Author = {Kenneth L. Clarkson},
Nobib = {y},
Note = {Survey talk given at SoCG; please see later slides mostly incorporating this material},
Title = {Geometry is Everywhere, Part {XLVII}: Metrics, Nets, Dimensions, and Measures},
Year = {2008}}
@inproceedings{one_pass,
Author = {Kenneth L. Clarkson and David P. Woodruff},
Booktitle = {STOC '09: } # proc # {FortyFirst } # stoc,
Dvi = {one_pass/p.dvi},
Pdf = {one_pass/p.pdf},
Ps = {one_pass/p.ps.gz.ps},
Slides = {one_pass/t3/t.xml},
Slidesnote1 = {n},
Slidesnote2 = {n},
Title = {Numerical Linear Algebra in the Streaming Model},
Year = {2009},
Abstract = {
We give nearoptimal space bounds in the streaming model for linear algebra
problems that include estimation of matrix products, linear regression,
lowrank approximation, and
approximation of matrix rank. In the streaming model, sketches of input matrices
are maintained under updates of matrix entries; we prove results for
turnstile updates, given in an arbitrary order. We give the
first lower bounds known for the space needed by the sketches, for a given
estimation error $\epsilon$. We sharpen prior upper bounds, with respect to
combinations of
space, failure probability, and number of passes.
The sketch we use for matrix $A$ is simply $S^TA$, where $S$ is a sign matrix.
Our results include the following upper and lower bounds on the bits of
space needed for $1$pass
algorithms. Here $A$ is an $n\times d$ matrix, $B$ is an $n\times d'$ matrix,
and $c := d+d'$. These results are given for fixed failure probability;
for failure probability $\delta>0$,
the upper bounds require a factor of $\log(1/\delta)$ more space.
We assume the inputs have integer entries specified by
$O(\log(nc))$ bits, or $O(\log(nd))$ bits. The Frobenius matrix norm is
used.
 (Matrix Product)
 Output matrix $C$ with
$$ A^TBC  \leq \epsilon A B.$$
We show that $\Theta(c\epsilon^{2}\log(nc))$ space is needed.
 (Linear Regression)
 For $d'=1$, so that $B$ is a vector $b$, find $x$ so that
$$Axb \leq (1+\epsilon)\min_{x' \in \reals^d}  Ax'b .$$
We show that
$\Theta(d^2\epsilon^{1}\log(nd))$ space is needed.
 (Rank$k$ Approximation)
 Find matrix $\hat A_k$ of rank no more than $k$,
so that
$$ A  \hat A_k \leq (1+\epsilon)AA_k,$$
where $A_k$ is the best rank$k$ approximation to $A$.
Our lower bound is $\Omega(k\epsilon^{1}(n+d)\log(nd))$ space,
and we give a onepass algorithm matching this when $A$ is given
rowwise or columnwise. For general updates, we give a onepass algorithm
needing
$$
O(k\epsilon^{2}(n + d/\epsilon^2)\log(nd))
$$
space. We also give upper and lower bounds
for algorithms using multiple passes, and a bicriteria
lowrank approximation.
}}
@article{multi_cover,
Appearedin = {SoCG '09: } # proc # {TwentyFifth } # socg,
Author = {Chandra Chekuri and Kenneth L. Clarkson and Sariel HarPeled},
Ayear = {2009},
Journal = talg,
Number = 1,
Pdf = {multi_cover/set_cover.pdf},
Slidesnote1 = {n},
Slidesnote2 = {n},
Title = {On the Set MultiCover Problem in Geometric Settings},
Url = {http://arxiv.org/abs/0909.0537},
Volume = 9,
Year = 2012,
Abstract = {
We consider the set multicover problem in geometric
settings. Given a set of points $P$ and a collection of
geometric shapes (or sets) $\cal F$, we wish to find a minimum
cardinality subset of $\cal F$ such that each point $p \in
P$ is covered by (contained in) at least $\textrm{d}(p)$
sets. Here $\textrm{d}(p)$ is an integer demand (requirement) for
$p$. When the demands $\textrm{d}(p)=1$ for all $p$, this is
the standard set cover problem. The set cover problem in geometric
settings admits an approximation ratio that is better than that
for the general version. In this paper, we show that similar
improvements can be obtained for the multicover problem as well.
In particular, we obtain an $O(\log\ OPT)$ approximation for set
systems of bounded VCdimension, and an $O(1)$ approximation for
covering points by halfspaces in three dimensions and for some
other classes of shapes.
},
BdskUrl1 = {http://arxiv.org/abs/0909.0537}}
@inproceedings{schema_covering,
Author = {Barna Saha and Ioana Stanoi and Kenneth L. Clarkson},
Bibsource = {DBLP, http://dblp.unitrier.de},
Booktitle = {ICDE},
Pages = {285296},
Title = {Schema covering: a step towards enabling reuse in information integration},
Url = {http://dx.doi.org/10.1109/ICDE.2010.5447853},
Year = {2010},
BdskUrl1 = {http://dx.doi.org/10.1109/ICDE.2010.5447853}}
@incollection{graph_anon,
Author = {Kenneth Clarkson and Kun Liu and Evimaria Terzi},
Booktitle = {Link Mining: Models Algorithms and Applications},
Editors = {C. Faloutsos, J. Han and P. Yu},
Title = {Towards Identity Anonymization in Social Networks},
Year = {2010}}
@inproceedings{sich,
Author = {Kenneth L. Clarkson and Wolfgang Mulzer and C. Seshadhri},
Booktitle = {SODA 2010: } # proc # {TwentyFirst } # soda,
Title = {Selfimproving Algorithms for Convex Hulls},
Year = {2010},
Abstract = {
We describe an algorithm for computing planar convex hulls in the
selfimproving model: given a sequence $I_1, I_2, \ldots$ of planar $n$point
sets, the upper convex hull $\conv(I)$ of each set $I$ is desired. We assume
that there exists a probability distribution $D$ on $n$point sets, such that
the inputs $I_j$ are drawn independently according to $D$. Furthermore, $D$ is
such that the individual points are distributed independently of each other.
In other words, the $i$'th point is distributed according to $D_i$. The
$D_i$'s can be arbitrary but are independent of each other. The distribution
$D$ is not known to the algorithm in advance. After a learning phase of
$n^\epsilon$ rounds, the expected time to compute $\conv(I)$ is $O(n +
H(\conv(I)))$. Here, $H(\conv(I))$ is the entropy of the output, which is a
lower bound for the expected running time of any algebraic computation
tree that computes the convex hull. (More precisely, $H(\conv(I))$ is the
minimum entropy of any random variable that maps $I$ to a description of
$\conv(I)$ and to a labeling scheme that proves nonextremality for every point
in $I$ not on the hull.) Our algorithm is thus asymptotically optimal for $D$.
},
Annote = {Bug found! Please see our later paper.}}
@article{sketchopt,
Appearedin = {FOCS 2010: } # proc # {FiftyFirst } # focs,
Author = {Kenneth L. Clarkson and Elad Hazan and David P. Woodruff},
Ayear = 2010,
Demo = {sketchopt/t2/demo/demo.svg},
Journal = jacm,
Number = 5,
Slides = {sketchopt/t2/t.html},
Title = {Sublinear Optimization for Machine Learning},
Url = {http://arxiv.org/abs/1010.4408},
Volume = 59,
Year = 2012,
Abstract = {
We give sublineartime approximation algorithms for some optimization problems
arising in machine learning, such as training linear classifiers and finding
minimum enclosing balls. Our algorithms can be extended to some kernelized
versions of these problems, such as SVDD, hard margin SVM, and $L_2$SVM, for
which sublineartime algorithms were not known before. These new algorithms use
a combination of a novel sampling techniques and a new multiplicative update
algorithm. We give lower bounds which show the running times of many of our
algorithms to be nearly best possible in the unitcost RAM model. We also give
implementations of our algorithms in the semistreaming setting, obtaining the
first low pass polylogarithmic space and sublinear time algorithms
achieving arbitrary approximation factor.
},
BdskUrl1 = {http://arxiv.org/abs/1010.4408}}
@unpublished{aarhus_school,
Author = {Kenneth L. Clarkson},
Demo = {socg_08/energy_demo/demo.svg},
Nobib = {y},
Note = {Tutorial for MADALGO & CTIC Summer School on HighDimensional Geometric Computing, Aarhus University, Denmark},
Slides = {aarhus_school/t.html},
Slidesnote1 = {y},
Slidesnote2 = {y},
Title = {Metric Spaces, Nets, Dimensions, and Search},
Year = {2011},
Month = {August},
Abstract = {
The idea of a metric space is among the most basic of geometric concepts,
and so appears in a great variety of applications and algorithms, sometimes
in disguise. This is a light survey of concepts and constructions
associated with metric spaces, including:
 Metric transformations
 `\epsilon`nets, the greedy algorithm, and applications
 A nongreedy algorithm and a few non`\epsilon`nets
 Box dimension and coping with finiteness
 Definitions of dimension that make sense for finite sets
 Estimation of dimension using finite samples
 Filtrations
 Of random subsets, kmedians, `\epsilon`nets
 Neighbors, generalizing "Delaunay neighbors"
 For witness complexes and NN searching
 Interpolation
 Interpolation of scattered data, Laplacians
 Interpolation in metric spaces
 A curious approach via "witness stealing"
A general theme here is "coping with finiteness", in trying to
apply concepts developed for infinite sets to a finite setting,
or finding frameworks that apply in both settings. Another theme
is the extent to which some constructions allow the resolution,
or scale of measurement, to be determined by the data.
},
Annote = {
This is yet another version of a long
continuing
interest,
with pointers to work on meshes, and
nearest neighbor searching.
The animations' javascript code is not for the squeamish.
}}
@unpublished{paris_school,
Author = {Kenneth L. Clarkson},
Demo = {paris_school/demo_centroid/demo.svg},
Nobib = {y},
Note = {Tutorial for kickoff workshop of CG Learning Project},
Slides = {paris_school/t.html},
Slidesnote1 = {y},
Slidesnote2 = {y},
Title = {Geometric Optimization and/or/in Machine Learning, and Related Topics},
Year = {2011},
Month = {June},
Abstract = {
The outline:
 Some geometric problems, algorithms, analysis
 Approximation algorithms
 First order methods (no second derivatives)
 Analysis using online convex optimization formalism
 But the problems and algorithms are not online
 Fast randomized approximate evaluation of dot products
 Matrix Bernstein and applications
 A primaldual algorithm
 (Significant) algorithmic results are joint with Elad Hazan and David Woodruff
}}
@article{sime,
Appearedas = {Selfimproving algorithms for coordinatewise maxima},
Appearedin = {SoCG '12: } # proc # {TwentyEighth } # socg,
Author = {Kenneth L. Clarkson and Wolfgang Mulzer and C. Seshadhri},
Ayear = {2012},
Title = {Selfimproving Algorithms for CoordinateWise Maxima and Convex Hulls},
Url = {http://arxiv.org/abs/1211.0952},
Abstract = {
Finding the coordinatewise maxima and the convex
hull of a planar point set are probably the most
classic problems in computational geometry. We
consider these problems in the selfimproving setting.
Here, we have $n$ distributions $\cD_1, \ldots, \cD_n$
of planar points. An input point set $(p_1, \ldots, p_n)$
is generated by taking an independent sample $p_i$
from each $\cD_i$, so the input is distributed
according to the product $\cD = \prod_i \cD_i$.
A selfimproving algorithm repeatedly gets inputs
from the distribution $\cD$ (which is a priori
unknown), and it tries to optimize its running time
for $\cD$. The algorithm uses the first few inputs to
learn salient features of the distribution $\cD$, before
it becomes finetuned to $\cD$. Let $\OPTMAX_\cD$
(resp. $\OPTCH_\cD$) be the expected depth
of an optimal linear comparison tree computing
the maxima (resp. convex hull) for $\cD$.
Our maxima algorithm eventually achieves expected
running time $O(\OPTMAX_\cD + n)$.
Furthermore, we give a selfimproving algorithm for convex hulls
with expected running time $O(\OPTCH_\cD + n\log\log n)$.
Our results require new tools for understanding linear
comparison trees. In particular, we convert
a general linear comparison tree to a restricted
version that can then be related to the running time
of our algorithms. Another interesting feature
is an interleaved search procedure
to determine the likeliest point to be extremal
with minimal computation. This allows our algorithms
to be competitive with the optimal algorithm for
$\cD$.
},
Annote = {Simpler and more verifiable algorithm for convex hulls than
the previous one of ours, whose proof is buggy.
},
BdskUrl1 = {http://arxiv.org/abs/1211.0952}}
@inproceedings{FCT,
Author = {Kenneth L. Clarkson and Petros Drineas and Malik MagdonIsmail and Michael W. Mahoney and Xiangrui Meng and David P. Woodruff},
booktitle={Proceedings of the TwentyFourth Annual ACMSIAM Symposium on Discrete Algorithms},
organization={SIAM},
Pages = {466477},
Title = {The Fast {C}auchy Transform and Faster Robust Linear Regression},
Url = {http://arxiv.org/abs/1207.4684},
Year = {2013},
Abstract = {We provide fast algorithms for overconstrained $\ell_p$ regression and
related problems: for an $n\times d$ input matrix $A$ and vector $b\in\R^n$,
in $O(nd\log n)$ time we reduce the problem $\min_{x\in\R^d} \norm{Axb}_p$
to the same problem with input matrix $\tilde A$ of dimension $s \times d$
and corresponding $\tilde b$ of dimension $s\times 1$.
Here, $\tilde A$ and $\tilde b$ are a coreset for the
problem, consisting of sampled and rescaled rows of $A$ and $b$;
and $s$ is independent of $n$ and polynomial in $d$.
Our results improve on the best previous algorithms when $n\gg d$, for all
$p\in [1,\infty)$ except $p=2$; in particular, they improve the
$O(nd^{1.376+})$ running time of Sohler and Woodruff (STOC, 2011) for
$p=1$, that uses asymptotically fast matrix multiplication, and the
$O(nd^5\log n)$ time of Dasgupta et al. (SICOMP, 2009) for general
$p$, that uses ellipsoidal rounding.
We also provide a suite of improved results for finding wellconditioned
bases via ellipsoidal rounding, illustrating tradeoffs between running time
and conditioning quality, including a onepass conditioning algorithm for
general $\ell_p$ problems.
To complement this theory, we provide a detdailed empirical evaluation of
implementations of our algorithms for $p=1$, comparing them with several
related algorithms.
Among other things, our empirical results clearly show that, in the
asymptotic regime, the theory is a very good guide to the practical
performance of these algorithms.
Our algorithms use our faster constructions of wellconditioned bases for
$\ell_p$ spaces and, for $p=1$, a fast subspace embedding of independent
interest that we call the Fast Cauchy Transform:
a matrix $\Pi: \R^n\mapsto \R^{O(d\log d)}$, found obliviously to $A$, that
approximately preserves the $\ell_1$ norms:
that is, $\norm{Ax}_1 \approx \norm{\Pi Ax}_1$, for all $x$, with distortion
$O(d^{2+\eta} \log d)$, for an arbitrarily small constant $\eta>0$;
and, moreover, $\Pi A$ can be computed in
$O(nd\log d)$ time.
The techniques underlying our Fast Cauchy Transform include fast
JohnsonLindenstrauss transforms, lowcoherence matrices, and rescaling by
Cauchy random variables.},
BdskUrl1 = {http://arxiv.org/abs/1207.4684}}
@inproceedings{sparse_embed,
Author = {Kenneth L. Clarkson and David P. Woodruff},
Booktitle = stoc,
Note = {Cowinner, Best Paper Award.},
Pages = {8190},
Slides = {sparse_embed/t.html},
Slidesnote1 = {y},
Title = {Low rank approximation and regression in input sparsity time},
Url = {http://arxiv.org/abs/1207.6365},
Year = {2013},
Abstract = {
We design a new distribution over $\poly(r
\eps^{1}) \times n$ matrices S so that for any
fixed $n\times d$ matrix A of rank r, with
probability at least 9/10, $\norm{SAx}_2 = (1 \pm
\eps)\norm{Ax}_2$ simultaneously for all $x\in\R^d$.
Such a matrix $S$ is called a subspace
embedding Furthermore, SA can be computed in
$O(\nnz{A})+ \poly(d \eps^{1})$ time, where
$\nnz{A}$ is the number of nonzero entries of A.
This improves over all previous subspace embeddings,
which required at least $\Omega(nd\log d)$ time to
achieve this property. We call our matrices $S$
sparse embedding matrices.
Using our sparse embedding matrices, we obtain the fastest
known algorithms for $(1+\eps)$approximation for
overconstrained leastsquares regression, lowrank
approximation, approximating all leverage scores, and
$\ell_p$regression. The leading order term in the time
complexity of our algorithms is $O(\nnz{A})$ or
$O(\nnz{A}\log n)$. We optimize the loworder
$\poly(d/\eps)$ terms in our running times (or for rank$k$
approximation, the $n*\poly(k/\eps)$ term), and show various
tradeoffs. For instance, we also use our methods to design
new preconditioners that improve the dependence on $\eps$ in
least squares regression to $\log 1/\eps$. Finally, we
provide preliminary experimental results which suggest that
our algorithms are competitive in practice. },
Annote = {See also Jelani Nelson and Huy L. Nguyen's OSNAP work,
and a related paper
by Xiangrui Meng and Michael Mahoney.},
BdskUrl1 = {http://arxiv.org/abs/1207.6365}}
@inproceedings{sample_sketch,
Author = {Kenneth L. Clarkson and David P. Woodruff},
booktitle={Proceedings of the TwentySixth Annual ACMSIAM Symposium on Discrete Algorithms},
pages={921939},
year={2015},
organization={SIAM},
Title = {Sketching for ${M}$Estimators: A Unified Approach to Robust Regression},
Pdf = {sample_sketch/p.pdf},
slides={sample_sketch/t2/t.html},
Slidesnote1 = {n},
Slidesnote2 = {n},
Abstract = {
We give algorithms for the $M$estimators $\min_x \norm{Axb}_G$, where $A\in\R^{n\times d}
$ and $b\in\R^n$, and $\norm{y}_G$ for $y\in\R^n$ is specified by a cost function $G:\R\mapsto \R^{\geq 0}$,
with $\norm{y}_G \equiv \sum_i G(y_i)$. The $M$estimators generalize $\ell_p$ regression, for which
$G(x)=x^p$. We first show that the Huber measure can be computed up to relative error $
\epsilon$ in $O(\nnz{A}\log n+ \poly(d (\log n)/\eps))$ time, where $\nnz{A}$ denotes the number of
nonzero entries of the matrix $A$. Huber is arguably the most widely used $M$estimator, enjoying
the robustness properties of $\ell_1$ as well as the smoothness properties of $\ell_2$.
We next develop algorithms for general $M$estimators. We analyze the $M$sketch, which is
a variation of a sketch introduced by Verbin and Zhang in the context of estimating the earthmover
distance. We show that the $M$sketch can be used much more generally for sketching any $M$
estimator provided $G$ has growth that is at least linear and at most quadratic.
Using the $M$sketch we solve the $M$estimation problem in $O(\nnz{A} + \poly(d \log n))$ time for
any such $G$ that is convex, making a single pass over the matrix and finding a solution whose
residual error is within a constant factor of optimal, with high probability.
},
Annote = {Results for regression generalized in following paper},
}
@unpublished{g2s3,
Author = {Kenneth L. Clarkson},
Nobib = {y},
Note = {Tutorial for Gene Golub Summer School},
Slides = {http://kenclarkson.org/G2S3_2015/},
Slidesnote1 = {y},
Slidesnote2 = {y},
Title = {Sketching for Matrix Computations},
Year = {2015},
Month = {July},
Abstract = {
The outline:
}
}
@inproceedings{L1L2,
Author = {Kenneth L. Clarkson and David P. Woodruff},
booktitle={Foundations of Computer Science (FOCS), 2015 IEEE 56th Annual Symposium on},
pages={310329},
year={2015},
organization={IEEE},
Slides={L1L2/t/},
Slidesnote1 = {y},
Slidesnote2 = {y},
Title = {Input Sparsity and Hardness for Robust Subspace Approximation},
Url = {http://arxiv.org/abs/1510.06073},
Year = {2015},
Abstract = {
In the subspace approximation problem, we seek a $k$dimensional subspace $F$ of $\R^d$ that minimizes
the sum of $p$th powers of Euclidean distances to a given set of $n$ points $a_1, \ldots, a_n \in \R^d$,
for $p \geq 1$.
More generally than minimizing $\sum_i \dist(a_i,F)^p$,
we may wish to minimize $\sum_i M(\dist(a_i,F))$ for some loss function $M()$, for example, $M$Estimators, which
include the Huber and Tukey loss functions.
Such subspaces provide alternatives
to the singular value decomposition (SVD), which is the $p=2$ case, finding
such an $F$ that minimizes the sum of squares of distances. For $p \in [1,2)$,
and for typical $M$Estimators, the minimizing $F$ gives a solution that is more robust to
outliers than that provided by the SVD.
We give several algorithmic results for these robust subspace approximation problems.
We state our results as follows, thinking of the $n$ points as forming an $n \times d$ matrix $A$, and
letting $\nnz{A}$
denote the number of nonzero entries of $A$.
Our results hold for $p\in [1,2)$.
We use $\poly(n)$ to denote $n^{O(1)}$ as $n\rightarrow\infty$.
 For minimizing $\sum_i \dist(a_i,F)^p$,
we give an algorithm running in
\[
O(\nnz{A} + (n+d)\poly(k/\eps) + \exp(\poly(k/\eps)))
\]
time which outputs a $k$dimensional
subspace $F$ whose cost is at most a $(1+\eps)$factor larger than the optimum.
 We show that the problem of minimizing $\sum_i \dist(a_i, F)^p$
is NPhard, even to output a $(1+1/\poly(d))$approximation. This extends work of Deshpande et al. (SODA, 2011) which could only
show NPhardness or UGChardness for $p > 2$; their proofs critically rely on $p > 2$. Our work resolves an open question
of [Kannan Vempala, NOW, 2009]. Thus, there cannot be an algorithm running in time polynomial in $k$ and $1/\eps$ unless P = NP.
Together with prior work, this implies that the problem is NPhard for all $p \neq 2$.
 For loss functions for a wide class of $M$Estimators, we give a problemsize reduction:
for a parameter $K=(\log n)^{O(\log k)}$, our reduction takes
\[
O(\nnz{A}\log n + (n+d)\poly(K/\eps))
\]
time to reduce the problem to a constrained version involving matrices whose dimensions
are $\poly(K\eps^{1}\log n)$. We also give bicriteria solutions.
 Our techniques lead to the first $O(\nnz{A} + \poly(d/\eps))$ time algorithms for $(1+\eps)$approximate
regression for a wide class of convex $M$Estimators.
This improves prior results, which were $(1+\eps)$approximation for Huber regression only,
and $O(1)$approximation for a general class of $M$Estimators.
}
}
@unpublished{bc60,
Author = {Kenneth L. Clarkson},
Nobib = {y},
Annote = {Talk for Bernard's birthday; I didn't talk about everything in the abstract.},
Slides = {http://kenclarkson.org/60BC/},
demo = {60BC/demo/NRG2.html},
Slidesnote1 = {y},
Slidesnote2 = {y},
Title = {The Thrill Goes On: Bernard Yesterday and Today},
Year = {2016},
Month = {July},
Abstract = {
From the excruciatingly difficult to the achingly elegant, Bernard
Chazelle's work on algorithms, especially geometric or natural ones, has
been profoundly influential. I'll sketch a few examples that have been
inspiring to me, including 1dimensional range queries, lowstabbing
spanning trees, highorder Voronoi diagram construction, deterministic
constructions, and the senergy of a system.
}
}
@unpublished{self_improve_general,
Author = {Kenneth L. Clarkson},
Nobib = {y},
Annote={This work was done 20082012 by Nir Ailon, Bernard Chazelle, Ding Liu, Wolfgang Mulzer, C. Seshadhri, and the speaker.},
Slides = {http://kenclarkson.org/self_improve_general/t_simons/},
Slidesnote1 = {y},
Slidesnote2 = {y},
Title = {Selfimproving algorithms},
Year = {2016},
Month = {November},
Abstract = {
We investigate algorithms that learn to run faster: given a series of instances of a computational problem, these algorithms use characteristics of past instances to sharpen their performance for new instances. We have found such *selfimproving* algorithms for sorting a list of numbers, and for some problems on planar point sets: computing Delaunay triangulations, coordinatewise maxima, and convex hulls. Under the assumption that input instances are random variables, each algorithm begins with a training phase during which it adjusts itself to the distribution of the input instances; this is followed by a stationary regime in which the algorithm runs in its optimized version. In this setting, an algorithm must take expected time proportional to the input size `n`, and to the entropy `E` of the output: the input must be touched, and the output must be communicated. Our algorithms achieve `O(n+E)` expected running times in the stationary regime for all the problems mentioned except convex hulls, where `O(n\log\log n + E)` expected time is needed.
}
}
@ARTICLE{krr,
author = {Haim Avron and Kenneth L. Clarkson and David P. Woodruff},
title = {Faster Kernel Ridge Regression Using Sketching and Preconditioning},
journal = {ArXiv eprints},
url = {https://arxiv.org/abs/1611.03220},
primaryClass = "cs.NA",
keywords = {Computer Science  Numerical Analysis, Computer Science  Data Structures and Algorithms, Computer Science  Learning, Mathematics  Numerical Analysis},
year = 2016,
month = Nov,
abstract = {
Kernel Ridge Regression (KRR) is a simple yet powerful technique for
nonparametric regression whose computation amounts to solving a linear
system. This system is usually dense and highly illconditioned. In addition,
the dimensions of the matrix are the same as the number of data points, so
direct methods are unrealistic for largescale datasets. In this paper, we
propose a preconditioning technique for accelerating the solution of the
aforementioned linear system. The preconditioner is based on random feature
maps, such as random Fourier features, which have recently emerged as a
powerful technique for speeding up and scaling the training of kernelbased
methods, such as kernel ridge regression, by resorting to approximations.
However, random feature maps only provide crude approximations to the kernel
function, so delivering stateoftheart results by directly solving the
approximated system requires the number of random features to be very large.
We show that random feature maps can be much more effective in forming
preconditioners, since under certain conditions a nottoolarge number of
random features is sufficient to yield an effective preconditioner. We
empirically evaluate our method and show it is highly effective for datasets
of up to one million training examples.
}
}
@inproceedings{psdSoda,
title={Lowrank {PSD} approximation in inputsparsity time},
author={Kenneth L. Clarkson and David P. Woodruff},
booktitle= {SODA '17: } # proc # {TwentyEighth } # soda,
pages={20612072},
year={2017},
pdf = {http://kenclarkson.org/psdSoda/p.pdf},
Slides = {http://kenclarkson.org/psdSoda/index.html},
Slidesnote1 = {y},
Slidesnote2 = {y},
organization={Society for Industrial and Applied Mathematics},
abstract = {
We give algorithms for approximation by lowrank positive semidefinite (PSD) matrices.
For symmetric input matrix $A\in\R^{n\times n}$, target rank $k$, and error parameter $\eps>0$, one algorithm
finds with constant probability a PSD matrix $\tY$ of rank $k$ such that $\norm{A\tY}_F^2\le (1+\eps)\norm{A  A_{k,+}}_F^2$,
where $A_{k,+}$ denotes the best rank$k$ PSD approximation to $A$, and the norm
is Frobenius. The algorithm takes time
$O(\nnz{A}\log n) + n\poly((\log n)k/\eps) + \poly(k/\eps)$, where $\nnz{A}$ denotes the number of nonzero entries
of $A$, and $\poly(k/\eps)$ denotes a polynomial in $k/\eps$. (There are two different polynomials in the time bound.)
Here the output matrix $\tY$ has the form $CUC^\top$, where the $O(k/\eps)$ columns of $C$ are columns of $A$.
In contrast to prior work, we do not require the input matrix $A$ to be PSD, our output is rank $k$ (not larger), and
our running time is $O(\nnz{A}\log n)$ provided this is larger than $n \poly((\log n)k/\epsilon)$.
We give a similar algorithm that is faster and simpler, but whose rank$k$ PSD output does not involve columns
of $A$, and does not require $A$ to be symmetric. We
give similar algorithms for best rank$k$ approximation subject to the constraint of symmetry.
We also show that there are asymmetric input matrices that cannot have good symmetric columnselected approximations.
}
}
@inproceedings{ridge_reg,
title = {Sharper Bounds for Regularized Data Fitting},
author = {Haim Avron and Kenneth L. Clarkson and David P. Woodruff},
booktitle = {RANDOM '17: 21st International Workshop on Randomization and Computation},
url = {https://arxiv.org/abs/1611.03225},
year = {2017},
abstract = {
The technique of matrix sketching, such as the use of random projections,
has been shown in recent years to be a powerful tool for accelerating many important
statistical learning techniques. Research has so far focused largely on using
sketching for the ``vanilla'' unregularized versions of these techniques.
Here we study sketching methods for regularized variants of linear regression,
low rank approximations, and canonical correlation analysis. We study regularization
both in a fairly broad setting, and in the specific context of the popular and widely
used technique of ridge regularization; for the latter, as applied to each of these
problems, we show algorithmic resource bounds in which the
{\em statistical dimension} appears in places where in previous bounds
the rank would appear.
The statistical dimension is always smaller than the rank, and decreases as
the amount of regularization increases. In particular, for the ridge lowrank
approximation problem
$\min_{Y,X} \norm{YX  A}_F^2 + \lambda\norm{Y}_F^2 + \lambda\norm{X}_F^2$,
where $Y\in\R^{n\times k}$ and $X\in\R^{k\times d}$,
we give an approximation algorithm needing
\[
O(\nnz{A}) + \tO((n+d)\eps^{1}k \min\{k, \eps^{1}\sd_\lambda(Y^*)\})+ \tO(\eps^{8} \sd_\lambda(Y^*)^3)
\]
time, where $\sd_{\lambda}(Y^*)\le k$ is the statistical dimension of $Y^*$, $Y^*$ is an optimal $Y$,
$\eps$ is an error parameter, and $\nnz{A}$ is the number of nonzero entries of $A$.
We also study regularization in a much more general setting. For example, we obtain
sketchingbased algorithms for the lowrank approximation problem
$\min_{X,Y} \norm{YX  A}_F^2 + f(Y,X)$
where $f(\cdot,\cdot)$ is a regularizing function satisfying some very general
conditions (chiefly, invariance under orthogonal transformations).
}
}