Restricted bayes strategies for convex stochastic programs

1984 ◽  
Vol 33 (1) ◽  
pp. 109-116
Author(s):  
Raymond Nadeau ◽  
Radu Theodorescu
Keyword(s):  
Stochastic Programs

An SQP-Type Method and Its Application in Stochastic Programs

2003 ◽  
Vol 116 (1) ◽  
pp. 205-228 ◽  
Author(s):  
Z. Wei ◽  
L. Qi ◽  
X. Chen
Keyword(s):  
Type Method ◽  
Stochastic Programs

scholarly journals A Quasi-Monte-Carlo-Based Feasible Sequential System of Linear Equations Method for Stochastic Programs with Recourse

2017 ◽  
Vol 2017 ◽  
pp. 1-15
Author(s):  
Changyin Zhou ◽  
Rui Su ◽  
Zhihui Jiang
Keyword(s):  
Monte Carlo ◽  
Linear Equations ◽  
Inequality Constraints ◽  
System Of Linear Equations ◽  
Active Constraint ◽  
Stochastic Programs ◽  
Quasi Monte Carlo ◽  
Sequential System ◽  
Constraint Set ◽  
A New Technique

A two-stage stochastic quadratic programming problem with inequality constraints is considered. By quasi-Monte-Carlo-based approximations of the objective function and its first derivative, a feasible sequential system of linear equations method is proposed. A new technique to update the active constraint set is suggested. We show that the sequence generated by the proposed algorithm converges globally to a Karush-Kuhn-Tucker (KKT) point of the problem. In particular, the convergence rate is locally superlinear under some additional conditions.

Consistency of Sample Estimates of Risk Averse Stochastic Programs

2013 ◽  
Vol 50 (02) ◽  
pp. 533-541 ◽  
Author(s):  
Alexander Shapiro
Keyword(s):  
Stochastic Programming ◽  
Law Of Large Numbers ◽  
Risk Measures ◽  
Regularity Conditions ◽  
Distributed Data ◽  
Risk Averse ◽  
Asymptotic Consistency ◽  
Stochastic Programs ◽  
Large Numbers ◽  
Independent Identically Distributed

In this paper we study asymptotic consistency of law invariant convex risk measures and the corresponding risk averse stochastic programming problems for independent, identically distributed data. Under mild regularity conditions, we prove a law of large numbers and epiconvergence of the corresponding statistical estimators. This can be applied in a straightforward way to establish convergence with probability 1 of sample-based estimators of risk averse stochastic programming problems.

scholarly journals A note on decision rules for stochastic programs

1968 ◽  
Vol 2 (3) ◽  
pp. 305-311 ◽  
Author(s):  
David W. Walkup ◽  
Roger J.-B. Wets
Keyword(s):  
Decision Rules ◽  
Stochastic Programs

Nodal decomposition–coordination for stochastic programs with private information restrictions

2015 ◽  
Vol 48 (3) ◽  
pp. 283-297
Author(s):  
Eric Beier ◽  
Saravanan Venkatachalam ◽  
V. Jorge Leon ◽  
Lewis Ntaimo
Keyword(s):  
Private Information ◽  
Stochastic Programs
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