Skip to search formSkip to main content

You are currently offline. Some features of the site may not work correctly.

Semantic Scholar uses AI to extract papers important to this topic.

Highly Cited

2013

Highly Cited

2013

In this paper, we introduce a new stochastic approximation (SA) type algorithm, namely the randomized stochastic gradient (RSG… Expand

Is this relevant?

Highly Cited

2009

Highly Cited

2009

In this paper we consider optimization problems where the objective function is given in a form of the expectation. A basic… Expand

Is this relevant?

Highly Cited

2009

Highly Cited

2009

“SPbook”2009/5/4page iiiiiiiiiiDarinka DentchevaDepartment of Mathematical SciencesStevens Institute of TechnologyHoboken, NJ… Expand

Is this relevant?

Highly Cited

2003

Highly Cited

2003

We consider convex stochastic programs with an (approximate) initial probability distribution P having finite support supp P, i.e… Expand

Is this relevant?

Highly Cited

2000

Highly Cited

2000

Given a convex stochastic programming problem with a discrete initial probability distribution, the problem of optimal scenario… Expand

Is this relevant?

Highly Cited

2000

Highly Cited

2000

Abstract Markov decision processes (MDPs) have proven to be popular models for decision-theoretic planning, but standard dynamic… Expand

Is this relevant?

Highly Cited

2000

Highly Cited

2000

A major issue in any application of multistage stochastic programming is the representation of the underlying random data process… Expand

Is this relevant?

Highly Cited

1998

Highly Cited

1995

Highly Cited

1995

When formulating a stochastic programming problem, we usually start from a deterministic problem that we call underlying… Expand

Is this relevant?

Highly Cited

1989