Abstract
In many dynamic stochastic optimization problems in practice, the uncertain factors are best modeled as random variables with an infinite support. This results in infinite-dimensional optimization problems that can rarely be solved directly. Therefore, the random variables (stochastic processes) are often approximated by finitely supported ones (scenario trees), which result in finite-dimensional optimization problems that are more likely to be solvable by available optimization tools. This paper presents conditions under which such finite-dimensional optimization problems can be shown to epi-converge to the original infinite-dimensional problem. Epi-convergence implies the convergence of optimal values and solutions as the discretizations are made finer. Our convergence result applies to a general class of convex problems where neither linearity nor complete recourse are assumed.
| Original language | English |
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| Pages (from-to) | 245-256 |
| Number of pages | 12 |
| Journal | MATHEMATICS OF OPERATIONS RESEARCH |
| Volume | 30 |
| Issue number | 1 |
| DOIs | |
| Publication status | Published - Feb 2005 |