Learning Hard Optimization Problems: A Data Generation Perspective

v1v2 (latest)

Learning Hard Optimization Problems: A Data Generation Perspective

4 June 2021

Ferdinando Fioretto

Pascal Van Hentenryck

ArXiv (abs)PDF HTML

Papers citing "Learning Hard Optimization Problems: A Data Generation Perspective"

7 / 7 papers shown

Title
Approximating Solutions to the Knapsack Problem using the Lagrangian Dual Framework Mitchell Keegan Mahdi Abolghasemi 92 1 0 06 Dec 2023
Optimization-based Learning for Dynamic Load Planning in Trucking Service Networks Ritesh Ojha Wenbo Chen Hanyu Zhang Reem Khir A. Erera Pascal Van Hentenryck 100 2 0 08 Jul 2023
Self-Supervised Primal-Dual Learning for Constrained Optimization Seonho Park Pascal Van Hentenryck 81 51 0 18 Aug 2022
Fast Continuous and Integer L-shaped Heuristics Through Supervised Learning Eric Larsen Emma Frejinger B. Gendron Andrea Lodi 59 19 0 02 May 2022
Ensuring DNN Solution Feasibility for Optimization Problems with Convex Constraints and Its Application to DC Optimal Power Flow Problems Tianyu Zhao Xiang Pan Minghua Chen S. Low 84 10 0 15 Dec 2021
Towards Understanding the Unreasonable Effectiveness of Learning AC-OPF Solutions M. H. Dinh Ferdinando Fioretto M. Mohammadian K. Baker 23 1 0 22 Nov 2021
Fast Approximations for Job Shop Scheduling: A Lagrangian Dual Deep Learning Method James Kotary Ferdinando Fioretto Pascal Van Hentenryck 100 20 0 12 Oct 2021