arXiv subjects

Anastasios Sidiropoulos

Publications and source records attributed to Anastasios Sidiropoulos.

45 records · Page 3Linked to original sources

arXiv2013

A near-optimal approximation algorithm for Asymmetric TSP on embedded graphs

We present a near-optimal polynomial-time approximation algorithm for the asymmetric traveling salesman problem for graphs of bounded orientable or non-orientable genus. Our algorithm achieves an approximation factor of O(f(g)) on graphs with genus g, where f(n) is the best approximation factor achievable in polynomial time on arbitrary n-vertex graphs. In particular, the O(log(n)/loglog(n))-approximation algorithm for general graphs by Asadpour et al. [SODA 2010] immediately implies an O(log(g)/loglog(g))-approximation algorithm for genus-g graphs. Our result improves the O(sqrt(g)*log(g))-approximation algorithm of Oveis Gharan and Saberi [SODA 2011], which applies only to graphs with orientable genus g; ours is the first approximation algorithm for graphs with bounded non-orientable genus. Moreover, using recent progress on approximating the genus of a graph, our O(log(g) / loglog(g))-approximation can be implemented even without an embedding when the input graph has bounded degree. In contrast, the O(sqrt(g)*log(g))-approximation algorithm of Oveis Gharan and Saberi requires a genus-g embedding as part of the input. Finally, our techniques lead to a O(1)-approximation algorithm for ATSP on graphs of genus g, with running time 2^O(g)*n^O(1).

cs.DS↗

arXiv2013

Non-positive curvature, and the planar embedding conjecture

The planar embedding conjecture asserts that any planar metric admits an embedding into L_1 with constant distortion. This is a well-known open problem with important algorithmic implications, and has received a lot of attention over the past two decades. Despite significant efforts, it has been verified only for some very restricted cases, while the general problem remains elusive. In this paper we make progress towards resolving this conjecture. We show that every planar metric of non-positive curvature admits a constant-distortion embedding into L_1. This confirms the planar embedding conjecture for the case of non-positively curved metrics.

cs.CG↗

arXiv2012

Pathwidth, trees, and random embeddings

We prove that, for every $k=1,2,...,$ every shortest-path metric on a graph of pathwidth $k$ embeds into a distribution over random trees with distortion at most $c$ for some $c=c(k)$. A well-known conjecture of Gupta, Newman, Rabinovich, and Sinclair states that for every minor-closed family of graphs $F$, there is a constant $c(F)$ such that the multi-commodity max-flow/min-cut gap for every flow instance on a graph from $F$ is at most $c(F)$. The preceding embedding theorem is used to prove this conjecture whenever the family $F$ does not contain all trees.

math.MG↗

arXiv2012

Planarizing an Unknown Surface

It has been recently shown that any graph of genus g>0 can be stochastically embedded into a distribution over planar graphs, with distortion Olog (g+1)) [Sidiropoulos, FOCS 2010]. This embedding can be computed in polynomial time, provided that a drawing of the input graph into a genus-g surface is given. We show how to compute the above embedding without having such a drawing. This implies a general reduction for solving problems on graphs of small genus, even when the drawing into a small genus surface is unknown. To the best of our knowledge, this is the first result of this type.

cs.DS↗

arXiv2010

On Graph Crossing Number and Edge Planarization

Given an n-vertex graph G, a drawing of G in the plane is a mapping of its vertices into points of the plane, and its edges into continuous curves, connecting the images of their endpoints. A crossing in such a drawing is a point where two such curves intersect. In the Minimum Crossing Number problem, the goal is to find a drawing of G with minimum number of crossings. The value of the optimal solution, denoted by OPT, is called the graph's crossing number. This is a very basic problem in topological graph theory, that has received a significant amount of attention, but is still poorly understood algorithmically. The best currently known efficient algorithm produces drawings with $O(\log^2 n)(n + OPT)$ crossings on bounded-degree graphs, while only a constant factor hardness of approximation is known. A closely related problem is Minimum Edge Planarization, in which the goal is to remove a minimum-cardinality subset of edges from G, such that the remaining graph is planar. Our main technical result establishes the following connection between the two problems: if we are given a solution of cost k to the Minimum Edge Planarization problem on graph G, then we can efficiently find a drawing of G with at most $\poly(d)\cdot k\cdot (k+OPT)$ crossings, where $d$ is the maximum degree in G. This result implies an $O(n\cdot \poly(d)\cdot \log^{3/2}n)$-approximation for Minimum Crossing Number, as well as improved algorithms for special cases of the problem, such as, for example, k-apex and bounded-genus graphs.

cs.DS↗

arXiv2010

Optimal stochastic planarization

It has been shown by Indyk and Sidiropoulos [IS07] that any graph of genus g>0 can be stochastically embedded into a distribution over planar graphs with distortion 2^O(g). This bound was later improved to O(g^2) by Borradaile, Lee and Sidiropoulos [BLS09]. We give an embedding with distortion O(log g), which is asymptotically optimal. Apart from the improved distortion, another advantage of our embedding is that it can be computed in polynomial time. In contrast, the algorithm of [BLS09] requires solving an NP-hard problem. Our result implies in particular a reduction for a large class of geometric optimization problems from instances on genus-g graphs, to corresponding ones on planar graphs, with a O(log g) loss factor in the approximation guarantee.

cs.CG↗

arXiv2010

Randomly removing g handles at once

Indyk and Sidiropoulos (2007) proved that any orientable graph of genus $g$ can be probabilistically embedded into a graph of genus $g-1$ with constant distortion. Viewing a graph of genus $g$ as embedded on the surface of a sphere with $g$ handles attached, Indyk and Sidiropoulos' method gives an embedding into a distribution over planar graphs with distortion $2^{O(g)}$, by iteratively removing the handles. By removing all $g$ handles at once, we present a probabilistic embedding with distortion $O(g^2)$ for both orientable and non-orientable graphs. Our result is obtained by showing that the nimum-cut graph of Erickson and Har Peled (2004) has low dilation, and then randomly cutting this graph out of the surface using the Peeling Lemma of Lee and Sidiropoulos (2009).

cs.CG↗

arXiv2008

Inapproximability for metric embeddings into R^d

We consider the problem of computing the smallest possible distortion for embedding of a given n-point metric space into R^d, where d is fixed (and small). For d=1, it was known that approximating the minimum distortion with a factor better than roughly n^(1/12) is NP-hard. From this result we derive inapproximability with factor roughly n^(1/(22d-10)) for every fixed d\ge 2, by a conceptually very simple reduction. However, the proof of correctness involves a nontrivial result in geometric topology (whose current proof is based on ideas due to Jussi Vaisala). For d\ge 3, we obtain a stronger inapproximability result by a different reduction: assuming P \ne NP, no polynomial-time algorithm can distinguish between spaces embeddable in R^d with constant distortion from spaces requiring distortion at least n^(c/d), for a constant c>0. The exponent c/d has the correct order of magnitude, since every n-point metric space can be embedded in R^d with distortion O(n^{2/d}\log^{3/2}n) and such an embedding can be constructed in polynomial time by random projection. For d=2, we give an example of a metric space that requires a large distortion for embedding in R^2, while all not too large subspaces of it embed almost isometrically.

cs.CG↗

arXiv2007

On the Complexity of Processing Massive, Unordered, Distributed Data

An existing approach for dealing with massive data sets is to stream over the input in few passes and perform computations with sublinear resources. This method does not work for truly massive data where even making a single pass over the data with a processor is prohibitive. Successful log processing systems in practice such as Google's MapReduce and Apache's Hadoop use multiple machines. They efficiently perform a certain class of highly distributable computations defined by local computations that can be applied in any order to the input. Motivated by the success of these systems, we introduce a simple algorithmic model for massive, unordered, distributed (mud) computation. We initiate the study of understanding its computational complexity. Our main result is a positive one: any unordered function that can be computed by a streaming algorithm can also be computed with a mud algorithm, with comparable space and communication complexity. We extend this result to some useful classes of approximate and randomized streaming algorithms. We also give negative results, using communication complexity arguments to prove that extensions to private randomness, promise problems and indeterminate functions are impossible. We believe that the line of research we introduce in this paper has the potential for tremendous impact. The distributed systems that motivate our work successfully process data at an unprecedented scale, distributed over hundreds or even thousands of machines, and perform hundreds of such analyses each day. The mud model (and its generalizations) inspire a set of complexity-theoretic questions that lie at their heart.

cs.CC↗