Name
Title | ||
|---|---|---|
A learning-based algorithm to quickly compute good primal solutions for Stochastic Integer Programs |
Abstract | ||
|---|---|---|
We propose a novel approach using supervised learning to obtain near-optimal primal solutions for two-stage stochastic integer programming (2SIP) problems with constraints in the first and second stages. The goal of the algorithm is to predict a "representative scenario" (RS) for the problem such that, deterministically solving the 2SIP with the random realization equal to the RS, gives a near-optimal solution to the original 2SIP. Predicting an RS, instead of directly predicting a solution ensures first-stage feasibility of the solution. If the problem is known to have complete recourse, second-stage feasibility is also guaranteed. For computational testing, we learn to find an RS for a two-stage stochastic facility location problem with integer variables and linear constraints in both stages and consistently provide near-optimal solutions. Our computing times are very competitive with those of general-purpose integer programming solvers to achieve a similar solution quality. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1007/978-3-030-58942-4_7 | CPAIOR |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yoshua Bengio | 1 | 42677 | 3039.83 |
| Emma Frejinger | 2 | 3 | 2.45 |
| Andrea Lodi | 3 | 2198 | 152.51 |
| Patel Rahul | 4 | 0 | 0.68 |
| Sriram Sankaranarayanan | 5 | 80 | 3.65 |