Sequential Convex Approximations to Joint Chance Constrained Programs: A Monte Carlo Approach

Operations Research- United States

doi 10.1287/opre.1100.0910

Full Text

Abstract

Available in

Date

Authors

Publisher

Institute for Operations Research and the Management Sciences (INFORMS)

Related search

A Critical Note on Empirical (Sample Average, Monte Carlo) Approximation of Solutions to Chance Constrained Programs

IFIP Advances in Information and Communication Technology
Computer NetworksInformation SystemsManagementCommunications
2013English

Convex Relaxations and Approximations of Chance-Constrained AC-OPF Problems

IEEE Transactions on Power Systems
Electronic EngineeringPower TechnologyElectricalEnergy Engineering
2019English

A Sequential Monte Carlo Approach to Gene Expression Deconvolution

PLoS ONE
Multidisciplinary
2017English

Sequentially Constrained Monte Carlo

Computational Statistics and Data Analysis
StatisticsProbabilityApplied MathematicsComputational TheoryComputational MathematicsMathematics
2016English

A Joint Probability Approach to Flood Frequency Estimation Using Monte Carlo Simulation

2010English

Joint Haplotype Assembly and Genotype Calling via Sequential Monte Carlo Algorithm

BMC Bioinformatics
BiochemistryApplied MathematicsComputer Science ApplicationsStructural BiologyMolecular Biology
2015English

Monte-Carlo Approach for Power Estimation in Sequential Circuits

English

On Two-Stage Convex Chance Constrained Problems

2005English

Elements of Sequential Monte Carlo

Foundations and Trends in Machine Learning
Human-Computer InteractionArtificial IntelligenceSoftware
2019English

Amanote Research

Note-taking for researchers

Follow Amanote

© 2026 Amaplex Software S.P.R.L. All rights reserved.

Privacy PolicyRefund Policy