27 citations · 56 across the 7 of their papers we have counts for
11 papers · 1 filter
ROmodel: Modeling robust optimization problems in Pyomo
Johannes Wiebe, Ruth Misener
This paper introduces ROmodel, an open source Python package extending the modeling capabilities of the algebraic modeling language Pyomo to robust optimization problems. ROmodel h…
Solving the pooling problem at scale with extensible solver GALINI
Francesco Ceccon, Ruth Misener
This paper presents a Python library to model pooling problems, a class of network flow problems with many engineering applications. The library automatically generates a mixed-int…
Partition-based formulations for mixed-integer optimization of trained ReLU neural networks
Calvin Tsay, Jan Kronqvist, Alexander Thebelt +1
This paper introduces a class of mixed-integer formulations for trained ReLU neural networks. The approach balances model size and tightness by partitioning node inputs into a numb…
Between steps: Intermediate relaxations between big-M and convex hull formulations
Jan Kronqvist, Ruth Misener, Calvin Tsay
This work develops a class of relaxations in between the big-M and convex hull formulations of disjunctions, drawing advantages from both. The proposed "P-split" formulations split…
A robust approach to warped Gaussian process-constrained optimization
Johannes Wiebe, Inês Cecílio, Jonathan Dunlop +1
Optimization problems with uncertain black-box constraints, modeled by warped Gaussian processes, have recently been considered in the Bayesian optimization setting. This work intr…
Approximation Algorithms for Process Systems Engineering
Dimitrios Letsios, Radu Baltean-Lugojan, Francesco Ceccon +3
Designing and analyzing algorithms with provable performance guarantees enables efficient optimization problem solving in different application domains, e.g.\ communication network…