Learning to solve optimization problems with hard linear constraints
Constrained optimization problems have appeared in a wide variety of challenging real-
world problems, where constraints often capture the physics of the underlying system …
world problems, where constraints often capture the physics of the underlying system …
Rayen: Imposition of hard convex constraints on neural networks
This paper presents RAYEN, a framework to impose hard convex constraints on the output
or latent variable of a neural network. RAYEN guarantees that, for any input or any weights …
or latent variable of a neural network. RAYEN guarantees that, for any input or any weights …
[HTML][HTML] Warm-starting constraint generation for mixed-integer optimization: A machine learning approach
Abstract Mixed Integer Linear Programs (MILP) are well known to be NP-hard (Non-
deterministic Polynomial-time hard) problems in general. Even though pure optimization …
deterministic Polynomial-time hard) problems in general. Even though pure optimization …
End-to-end constrained optimization learning: A survey
J Kotary, F Fioretto, P Van Hentenryck… - arXiv preprint arXiv …, 2021 - arxiv.org
This paper surveys the recent attempts at leveraging machine learning to solve constrained
optimization problems. It focuses on surveying the work on integrating combinatorial solvers …
optimization problems. It focuses on surveying the work on integrating combinatorial solvers …
Learning differentiable solvers for systems with hard constraints
We introduce a practical method to enforce partial differential equation (PDE) constraints for
functions defined by neural networks (NNs), with a high degree of accuracy and up to a …
functions defined by neural networks (NNs), with a high degree of accuracy and up to a …
Learning with constraint learning: New perspective, solution strategy and various applications
The complexity of learning problems, such as Generative Adversarial Network (GAN) and its
variants, multi-task and meta-learning, hyper-parameter learning, and a variety of real-world …
variants, multi-task and meta-learning, hyper-parameter learning, and a variety of real-world …
Learning linear programs from data
Linear Programming lies at the core of mathematical modelling and optimization. Designing
linear programs (LPs) is a difficult and expensive process, as it requires both mathematical …
linear programs (LPs) is a difficult and expensive process, as it requires both mathematical …
Polynomial optimization: Enhancing RLT relaxations with conic constraints
B González-Rodríguez, R Alvite-Pazó… - arXiv preprint arXiv …, 2022 - arxiv.org
Conic optimization has recently emerged as a powerful tool for designing tractable and
guaranteed algorithms for non-convex polynomial optimization problems. On the one hand …
guaranteed algorithms for non-convex polynomial optimization problems. On the one hand …
[HTML][HTML] Optimization with constraint learning: A framework and survey
Many real-life optimization problems frequently contain one or more constraints or objectives
for which there are no explicit formulae. If however data on feasible and/or infeasible states …
for which there are no explicit formulae. If however data on feasible and/or infeasible states …
Ensuring DNN solution feasibility for optimization problems with linear constraints
We propose preventive learning as the first framework to guarantee Deep Neural Network
(DNN) solution feasibility for optimization problems with linear constraints without post …
(DNN) solution feasibility for optimization problems with linear constraints without post …