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4008. Inverse Optimization for Imputing Constraint Parameters in Mathematical Programs
Invited abstract in session MC-41: Stochastic and Deterministic Global Optimization, stream Stochastic and Deterministic Global Optimization.
Monday, 12:30-14:00Room: 97 (building: 306)
Authors (first author is the speaker)
| 1. | ARCHIS GHATE
|
| Industrial and Systems Engineering, University of Minnesota |
Abstract
In a typical (forward) optimization problem, a decision-maker uses given values of model parameters to compute the values of decision variables. The goal in inverse optimization (IO) is instead to infer parameters that render given values of decision variables optimal. Most papers on IO utilize duality to impute objective function parameters. A corresponding literature for imputing constraint parameters is essentially non-existent, even for linear programs. The difficulty is that these IO problems include nonconvex bilinear constraints and/or objectives. We will discuss models and solution algorithms designed to tackle these difficult problems. We will illustrate key ideas through the motivating problem of imputing transition probabilities in Markov decision processes (MDPs). If time permits, we will also extend these ideas to inverse semi-definite programs (SDPs) and inverse Quadratic Programs (QPs), where matrices on the left-hand-sides of constraints are unknown.
Keywords
- Continuous Optimization
- Convex Optimization
Status: accepted
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