Joint Spectral Radius and Path-Complete Graph Lyapunov Functions

We introduce the framework of path-complete graph Lyapunov functions for approximation of the joint spectral radius. The approach is based on the analysis of the underlying switched system via inequalities imposed among multiple Lyapunov functions associated to a labeled directed graph. Inspired by concepts in automata theory and symbolic dynamics, we define a class of … Read more

Approximating the Exponential, the Lanczos Method and an \tilde{O}(m)-Time Spectral Algorithm for Balanced Separator

We give a novel spectral approximation algorithm for the balanced separator problem that, given a graph G, a constant balance b \in (0,1/2], and a parameter \gamma, either finds an \Omega(b)-balanced cut of conductance O(\sqrt{\gamma}) in G, or outputs a certificate that all b-balanced cuts in G have conductance at least \gamma, and runs in … Read more

An FPTAS for Optimizing a Class of Low-Rank Functions Over a Polytope

We present a fully polynomial time approximation scheme (FPTAS) for optimizing a very general class of nonlinear functions of low rank over a polytope. Our approximation scheme relies on constructing an approximate Pareto-optimal front of the linear functions which constitute the given low-rank function. In contrast to existing results in the literature, our approximation scheme … Read more

A General Framework for Designing Approximation Schemes for Combinatorial Optimization Problems with Many Objectives Combined Into One

In this paper, we present a general framework for designing approximation schemes for combinatorial optimization problems in which the objective function is a combination of more than one function. Examples of such problems include those in which the objective function is a product or ratio of two linear functions, parallel machine scheduling problems with the … Read more

On the Robust Knapsack Problem

We consider an uncertain variant of the knapsack problem that arises when the exact weight of each item is not exactly known in advance but belongs to a given interval, and the number of items whose weight differs from the nominal value is bounded by a constant. We analyze the worsening of the optimal solution … Read more

On implementation of local search and genetic algorithm techniques for some combinatorial optimization problems

In this paper we propose the approach to solving several combinatorial optimization problems using local search and genetic algorithm techniques. Initially this approach was developed in purpose to overcome some difficulties inhibiting the application of above-mentioned techniques to the problems of the Questionnaire Theory. But when the algorithms were developed it became clear that them … Read more

Job-Shop Scheduling in a Body Shop

We study a generalized job-shop problem called the body shop scheduling problem (bssp). This problem arises from the industrial application of welding in a car body production line, where possible collisions between industrial robots have to be taken into account. bssp corresponds to a job-shop problem where the operations of a job have to follow … Read more

Approximation Theory of Matrix Rank Minimization and Its Application to Quadratic Equations

Matrix rank minimization problems are gaining a plenty of recent attention in both mathematical and engineering fields. This class of problems, arising in various and across-discipline applications, is known to be NP-hard in general. In this paper, we aim at providing an approximation theory for the rank minimization problem, and prove that a rank minimization … Read more

CONVEX HULL RELAXATION (CHR) FOR CONVEX AND NONCONVEX MINLP PROBLEMS WITH LINEAR CONSTRAINTS

The behavior of enumeration-based programs for solving MINLPs depends at least in part on the quality of the bounds on the optimal value and of the feasible solutions found. We consider MINLP problems with linear constraints. The convex hull relaxation (CHR) is a special case of the primal relaxation (Guignard 1994, 2007) that is very … Read more

A new, solvable, primal relaxation for convex nonlinear integer programming problems

The paper describes a new primal relaxation (PR) for computing bounds on nonlinear integer programming (NLIP) problems. It is a natural extension to NLIP problems of the geometric interpretation of Lagrangean relaxation presented by Geoffrion (1974) for linear problems, and it is based on the same assumption that some constraints are complicating and are treated … Read more