Variance reduction in Monte Carlo sampling-based optimality gap estimators for two-stage stochastic linear programming
From MaRDI portal
Publication:288402
DOI10.1007/s10589-015-9814-9zbMath1342.90119OpenAlexW2298876865WikidataQ57500065 ScholiaQ57500065MaRDI QIDQ288402
Rebecca Stockbridge, Güzin Bayraksan
Publication date: 25 May 2016
Published in: Computational Optimization and Applications (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/s10589-015-9814-9
Monte Carlo samplingLatin hypercube samplingvariance reductiontwo-stage stochastic programmingantithetic variatesoptimality gap estimation
Related Items (3)
An improved averaged two-replication procedure with Latin hypercube sampling ⋮ Variance reduction for sequential sampling in stochastic programming ⋮ Adaptive Sequential Sample Average Approximation for Solving Two-Stage Stochastic Linear Programs
Uses Software
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- The impact of sampling methods on bias and variance in stochastic linear programs
- Validation analysis of mirror descent stochastic approximation method
- Parallel processors for planning under uncertainty
- Asymptotic behavior of statistical estimators and of optimal solutions of stochastic optimization problems
- Monte Carlo (importance) sampling within a Benders decomposition algorithm for stochastic linear programs
- Asymptotic properties of statistical estimators in stochastic programming
- A simulation-based approach to two-stage stochastic programming with recourse
- A branch and bound method for stochastic global optimization
- Monte Carlo bounding techniques for determinig solution quality in stochastic programs
- On Latin hypercube sampling
- Duality and statistical tests of optimality for two stage stochastic programs
- Epi-convergent discretizations of stochastic programs via integration quadratures
- The sample average approximation method applied to stochastic routing problems: a computational study
- Simulation-based confidence bounds for two-stage stochastic programs
- Some large deviations results for Latin hypercube sampling
- A probability metrics approach for reducing the bias of optimality gap estimators in two-stage stochastic linear programming
- A stochastic programming approach for supply chain network design under uncertainty
- Variance reduction in sample approximations of stochastic programs
- Assessing solution quality in stochastic programs
- The empirical behavior of sampling methods for stochastic programming
- Monte Carlo and quasi-Monte Carlo sampling
- Linear Programming under Uncertainty
- A Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output from a Computer Code
- A Sequential Sampling Procedure for Stochastic Programming
- On Rates of Convergence for Stochastic Optimization Problems Under Non–Independent and Identically Distributed Sampling
- Monte Carlo Variance of Scrambled Net Quadrature
- Variance Reduction and Objective Function Evaluation in Stochastic Linear Programs
- Asymptotic Theory for Solutions in Statistical Estimation and Stochastic Programming
- A New Scenario Decomposition Method for Large-Scale Stochastic Optimization
- Fixed-Width Sequential Stopping Rules for a Class of Stochastic Programs
- Sensitivity of bond portfolio's behavior with respect to random movements in yield curve: a simulation study
This page was built for publication: Variance reduction in Monte Carlo sampling-based optimality gap estimators for two-stage stochastic linear programming
