Geometric Rounding: A Dependent Rounding Scheme for Allocation Problems

This paper presents a general technique to develop approximation algorithms for allocation problems with integral assignment constraints. The core of the method is a randomized dependent rounding scheme, called geometric rounding, which yields termwise rounding ratios (in expectation), while emphasizing the strong correlation between events. We further explore the intrinsic geometric structure and general theoretical … Read more

A Unified Theorem on SDP Rank Reduction

We consider the problem of finding a low-rank approximate solution to a system of linear equations in symmetric, positive semidefinite matrices. Specifically, let $A_1,\ldots,A_m \in \R^{n\times n}$ be symmetric, positive semidefinite matrices, and let $b_1,\ldots,b_m \ge 0$. We show that if there exists a symmetric, positive semidefinite matrix $X$ to the system $A_i \bullet X … Read more

A Note on Exchange Market Equilibria with Leontief’s Utility: Freedom of Pricing Leads to Rationality

We extend the analysis of [27] to handling more general utility functions: piece-wise linear functions, which include Leontief’s utility. We show that the problem reduces to the general analytic center model discussed in [27]. Thus, the same linear programming complexity bound applies to approximating the Fisher equilibrium problem with these utilities. More importantly, we show … Read more

Semidefinite Programming Based Algorithms for Sensor Network Localization

An SDP relaxation based method is developed to solve the localization problem in sensor networks using incomplete and inaccurate distance information. The problem is set up to find a set of sensor positions such that given distance constraints are satisfied. The nonconvex constraints in the formulation are then relaxed in order to yield a semidefinite … Read more

Theory of Semidefinite Programming for Sensor Network Localization

We analyze the semidefinite programming (SDP) based model and method for the position estimation problem in sensor network localization and other Euclidean distance geometry applications. We use SDP duality and interior–point algorithm theories to prove that the SDP localizes any network or graph that has unique sensor positions to fit given distance measures. Therefore, we … Read more

Approximating the Radii of Point Sets

We consider the problem of computing the outer-radii of point sets. In this problem, we are given integers $n, d, k$ where $k \le d$, and a set $P$ of $n$ points in $R^d$. The goal is to compute the {\em outer $k$-radius} of $P$, denoted by $\kflatr(P)$, which is the minimum, over all $(d-k)$-dimensional … Read more

A Path to the Arrow-Debreu Competitive Market Equilibrium

We present polynomial-time interior-point algorithms for solving the Fisher and Arrow-Debreu competitive market equilibrium problems with linear utilities and $n$ players. Both of them have the arithmetic operation complexity bound of $O(n^4\log(1/\epsilon))$ for computing an $\epsilon$-equilibrium solution. If the problem data are rational numbers and their bit-length is $L$, then the bound to generate an … Read more

A New Complexity Result on Solving the Markov Decision Problem

We present a new complexity result on solving the Markov decision problem (MDP) with $n$ states and a number of actions for each state, a special class of real-number linear programs with the Leontief matrix structure. We prove that, when the discount factor $\theta$ is strictly less than $1$, the problem can be solved in … Read more

A Multi-Exchange Local Search Algorithm for the Capacitated Facility Location Problem

We present a multi-exchange local search algorithm for approximating the capacitated facility location problem (CFLP), where a new local improvement operation is introduced that possibly exchanges multiple facilities simultaneously. We give a tight analysis for our algorithm and show that the performance guarantee of the algorithm is between $3+2\sqrt{2}-\epsilon$ and $3+2\sqrt{2}+\epsilon$ for any given constant … Read more

DSDP4 Software User Guide

DSDP4 is an implementation of the dual-scaling algorithm for semidefinite program ming. New features in this version include a Lanczos procedure for determining the step size, more precise primal solutions, a parallel solver, and improved performance on the standard test suites. CitationANL/MCS-TM-255; Mathematics and Computer Science Division; Argonne National Laboratory; Argonne, IL; March 2002ArticleDownload View … Read more