324. Solving the Optimal Experiment Design with Mixed-Integer Convex Methods
Invited abstract in session WC-4: Large Scale Optimization for Statistical Learning, stream Optimization for machine learning.
Wednesday, 14:00-16:00Room: B100/5013
Authors (first author is the speaker)
| 1. | Deborah Hendrych
|
| Zuse Institut Berlin | |
| 2. | Mathieu Besançon
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| Zuse Institute Berlin | |
| 3. | Sebastian Pokutta
|
| ZIB/TUB |
Abstract
We tackle the Optimal Experiment Design Problem, which consists of choosing experiments to run or observations to select from a finite set to estimate the parameters of a system. The objective is to maximize some measure of information gained about the system from the observations, leading to a convex integer optimization problem. We leverage Boscia.jl, a recent algorithmic framework, which is based on a nonlinear branch-and-bound algorithm with node relaxations solved to approximate optimality using Frank-Wolfe algorithms. One particular advantage of the method is its efficient utilization of the polytope formed by the original constraints which is preserved by the method, unlike alternative methods relying on epigraph-based formulations. We assess our method against both generic and specialized convex mixed-integer approaches. Computational results highlight the performance of our proposed method, especially on large and challenging instances.
Keywords
- Nonlinear mixed integer optimization
- First-order optimization
- Computational mathematical optimization
Status: accepted
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