Monte Carlo (importance) sampling within a Benders decomposition algorithm for stochastic linear programs

From MaRDI portal
Publication:1207838

DOI10.1007/BF02060936zbMath0773.90054OpenAlexW1981264388MaRDI QIDQ1207838

Gerd Infanger

Publication date: 16 May 1993

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

Full work available at URL: https://doi.org/10.1007/bf02060936




Related Items (43)

Variance reduction in Monte Carlo sampling-based optimality gap estimators for two-stage stochastic linear programmingChance-constrained problems and rare events: an importance sampling approachStatistical approximations for recourse constrained stochastic programsA moment-matching method to generate arbitrage-free scenariosNew bounding and decomposition approaches for MILP investment problems: multi-area transmission and generation planning under policy constraintsImportance Sampling in Stochastic Programming: A Markov Chain Monte Carlo ApproachCut sharing for multistage stochastic linear programs with interstage dependencyDuality and statistical tests of optimality for two stage stochastic programsMultistage stochastic programming: Error analysis for the convex caseOptimal Power Flow in Distribution Networks Under N – 1 Disruptions: A Multistage Stochastic Programming ApproachImportance sampling in stochastic optimization: an application to intertemporal portfolio choiceIntegrated network capacity expansion and traffic signal optimization problem: Robust bi-level dynamic formulationAugmented simulation methods for discrete stochastic optimization with recourseEfficient Stochastic Programming in JuliaThe Benders by batch algorithm: design and stabilization of an enhanced algorithm to solve multicut Benders reformulation of two-stage stochastic programsThe impact of sampling methods on bias and variance in stochastic linear programsSimulation-based confidence bounds for two-stage stochastic programsA probability metrics approach for reducing the bias of optimality gap estimators in two-stage stochastic linear programmingConfidence level solutions for stochastic programmingMulticut Benders decomposition algorithm for process supply chain planning under uncertaintyIdentifying effective scenarios in distributionally robust stochastic programs with total variation distanceSharing cuts under aggregated forecasts when decomposing multi-stage stochastic programsISMISIP: an inexact stochastic mixed integer linear semi-infinite programming approach for solid waste management and planning under uncertaintySimulation-Based Optimality Tests for Stochastic ProgramsVariance reduction for sequential sampling in stochastic programmingPlanning of municipal solid waste management systems under dual uncertainties: a hybrid interval stochastic programming approachMultistage scenario-based interval-stochastic programming for planning water resources allocationIdentification of optimal plans for municipal solid waste management in an environment of fuzziness and two-layer randomnessMulti-service multi-facility network design under uncertaintyVariance reduction in sample approximations of stochastic programsEpi-convergent discretizations of stochastic programs via integration quadraturesAdaptive multicut aggregation for two-stage stochastic linear programs with recourseStochastic programming for funding mortgage poolsInexact stochastic mirror descent for two-stage nonlinear stochastic programsAssessing solution quality in stochastic programsA scalable solution framework for stochastic transmission and generation planning problemsAugmented Markov Chain Monte Carlo Simulation for Two-Stage Stochastic Programs with RecourseIntelligent control and optimization under uncertainty with application to hydro powerA hybrid inexact-stochastic water management modelMonte Carlo bounding techniques for determinig solution quality in stochastic programsMulti-stage stochastic linear programs for portfolio optimizationInexact subgradient methods with applications in stochastic programmingUnnamed Item


Uses Software


Cites Work


This page was built for publication: Monte Carlo (importance) sampling within a Benders decomposition algorithm for stochastic linear programs