Approximation Algorithms for Indefinite Complex Quadratic Maximization Problems

In this paper we consider the following two types of complex quadratic maximization problems: (i) maximize $z^{\HH} Q z$, subject to $z_k^m=1$, $k=1,…,n$, where $Q$ is a Hermitian matrix with $\tr Q=0$ and $z\in \C^n$ is the decision vector; (ii) maximize $\re y^{\HH}Az$, subject to $y_k^m=1$, $k=1,…,p$, and $z_l^m=1$, $l=1,…,q$, where $A\in \C^{p\times q}$ and … Read more

Approximating K-means-type clustering via semidefinite programming

One of the fundamental clustering problems is to assign $n$ points into $k$ clusters based on the minimal sum-of-squares(MSSC), which is known to be NP-hard. In this paper, by using matrix arguments, we first model MSSC as a so-called 0-1 semidefinite programming (SDP). We show that our 0-1 SDP model provides an unified framework for … Read more

Semidefinite Programming Based Approaches to Home-away Assignment Problems in Sports Scheduling

For a given schedule of a round-robin tournament and a matrix of distances between homes of teams, an optimal home-away assignment problem is to find a home-away assignment that minimizes the total traveling distance. We propose a technique to transform the problem to MIN RES CUT. We apply Goemans and Williamson’s 0.878-approximation algorithm for MAX … Read more

A PTAS for the minimization of polynomials of fixed degree over the simplex

We consider the problem of computing the minimum value $p_{\min}$ taken by a polynomial $p(x)$ of degree $d$ over the standard simplex $\Delta$. This is an NP-hard problem already for degree $d=2$. For any integer $k\ge 1$, by minimizing $p(x)$ over the set of rational points in $\Delta$ with denominator $k$, one obtains a hierarchy … Read more

Sums of Random Symmetric Matrices and Applications

Let B_i be deterministic symmetric m\times m matrices, and \xi_i be independent random scalars with zero mean and “of order of one” (e.g., \xi_i are Gaussian with zero mean and unit standard deviation). We are interested in conditions for the “typical norm” of the random matrix S_N = \xi_1B_1+…+\xi_NB_N to be of order of 1. … Read more

Complex Quadratic Optimization and Semidefinite Programming

In this paper we study the approximation algorithms for a class of discrete quadratic optimization problems in the Hermitian complex form. A special case of the problem that we study corresponds to the max-3-cut model used in a recent paper of Goemans and Williamson. We first develop a closed-form formula to compute the probability of … Read more

Faster approximation algorithms for packing and covering problems

We adapt a method due to Nesterov so as to obtain an algorithm for solving block-angular fractional packing or covering problems to relative tolerance epsilon, while using a number of iterations that grows polynomially in the size of the problem and whose dependency on epsilon is proportional to 1/epsilon. Citation CORC report TR-2004-09, Computational Optimization … Read more

Approximate fixed-rank closures of set covering problems

We show that for any fixed rank, the closure of a set covering problem (and related problems) can be approximated in polynomial time — we can epsilon-approximate any linear program over the closure in polynomial time. Citation CORC report TR-2003-01, Computational Optimization Research Center, Columbia University Article Download View Approximate fixed-rank closures of set covering … Read more

On cost matrices with two and three distinct values of Hamiltonian paths and cycles

Polynomially testable characterization of cost matrices associated with a complete digraph on $n$ nodes such that all the Hamiltonian cycles (tours) have the same cost is well known. Tarasov~\cite{TARA81} obtained a characterization of cost matrices where tour costs take two distinct values. We provide a simple alternative characterization of such cost matrices that can be … Read more

Batched Bin Packing

We introduce and study the batched bin packing problem (BBPP), a bin packing problem in which items become available for packing incrementally, one batch at a time. A batched algorithm must pack a batch before the next batch becomes known. A batch may contain several items; the special case when each batch consists of merely … Read more