Probabilistic Models for Linear Programming

DOI10.1287/moor.16.4.671zbMath0751.90056OpenAlexW2137427562MaRDI QIDQ3989814

Michael J. Todd

Publication date: 28 June 1992

Published in: Mathematics of Operations Research (Search for Journal in Brave)

Full work available at URL: https://hdl.handle.net/1813/8719

zbMATH Keywords

probabilistic modelsrandom linear programmingKarmarkar's algorithm

Mathematics Subject Classification ID

Linear programming (90C05) Stochastic programming (90C15)

Related Items (14)

Interior-point methods: Worst case and average case analysis of a phase-I algorithm and a termination procedure. ⋮ Random matrix theory for the analysis of the performance of an analog computer: a scaling theory ⋮ Smoothed analysis of condition numbers and complexity implications for linear programming ⋮ Halting time is predictable for large models: a universality property and average-case analysis ⋮ Scaling and universality of the complexity of analog computation ⋮ Probabilistic analysis of a differential equation for linear programming ⋮ Generation techniques for linear programming instances with controllable properties ⋮ On the probabilistic complexity of finding an approximate solution for linear programming ⋮ A universal scaling theory for complexity of analog computation ⋮ Average case complexity results for a centering algorithm for linear programming problems under Gaussian distributions ⋮ Average number of iterations of some polynomial interior-point -- algorithms for linear programming ⋮ Selected bibliography on degeneracy ⋮ Degeneracy graphs: Theory and applications. An updated survey ⋮ An Extended Necessity Measure Maximisation Incorporating the Trade-Off between Robustness and Satisfaction in Fuzzy LP Problems

This page was built for publication: Probabilistic Models for Linear Programming