Nonlinear Formations and Improved Randomized Approximation Algorithms for Multiway and Multicut Problems

We introduce nonlinear formulations of the multiway cut and multicut problems. By simple linearizations of these formulations we derive several well known formulations and valid inequalities as well as several new ones. Through these formulations we establish a connection between the multiway cut and the maximum weighted independent set problem that leads to the study of the tightness of several LP formulations for the multiway cut problem through the theory of perfect graphs. We also introduce a new randomized rounding argument to study the worst case bound of these formulations, obtaining a new bound of 2a(H)(1 - ) for the multicut problem, where ac(H) is the size of a maximum independent set in the demand graph H

Constructing approximation algorithms via linear programming relaxations : primal dual and randomized rounding techniques

Thesis (Ph. D.)--Massachusetts Institute of Technology, Sloan School of Management, 1996.Includes bibliographical references (p. 168-179).by Chung-Piaw Teo.Ph.D

From valid inequalities to heuristics : a unified view of primal-dual approximation algortithms [sic] in covering problems

Includes bibliographical references (p. 26-27).HD28 .M414 no.3707-, 94,

Systems Optimization: Models and Computation

A computational and application-oriented introduction to the modeling of large-scale systems in a wide variety of decision-making domains and the optimization of such systems using state-of-the-art optimization software. Application domains include transportation and logistics, pattern classification, structural design, financial engineering, and telecommunications system planning. Modeling tools and techniques covered include linear, network, discrete, and nonlinear programming, heuristic methods, sensitivity and postoptimality analysis, decomposition methods for large-scale systems, and stochastic programming. From the course home page: Course Description An applications-oriented course on the modeling of large-scale systems in decision-making domains and the optimization of such systems using state-of-the-art optimization tools. Application domains include: transportation and logistics planning, pattern classification and image processing, data mining, design of structures, scheduling in large systems, supply-chain management, financial engineering, and telecommunications systems planning. Modeling tools and techniques include linear, network, discrete and nonlinear optimization, heuristic methods, sensitivity and post-optimality analysis, decomposition methods for large-scale systems, and stochastic optimization

Nonlinear formulations and improved randomized approximation algorithms for multiway and multicut problems

HD28 .M414 no.3838-95,