27 citations · 45 across the 13 of their papers we have counts for
14 papers
Adaptive Solution Prediction for Combinatorial Optimization
Yunzhuang Shen, Yuan Sun, Xiaodong Li +2
This paper aims to predict optimal solutions for combinatorial optimization problems (COPs) via machine learning (ML). To find high-quality solutions efficiently, existing work use…
Enhancing Column Generation by a Machine-Learning-Based Pricing Heuristic for Graph Coloring
Yunzhuang Shen, Yuan Sun, Xiaodong Li +2
Column Generation (CG) is an effective method for solving large-scale optimization problems. CG starts by solving a sub-problem with a subset of columns (i.e., variables) and gradu…
Learning Primal Heuristics for Mixed Integer Programs
Yunzhuang Shen, Yuan Sun, Andrew Eberhard +1
This paper proposes a novel primal heuristic for Mixed Integer Programs, by employing machine learning techniques. Mixed Integer Programming is a general technique for formulating…
Convergence Rates for Boundedly Regular Systems
Ernö Robert Csetnek, Andrew Eberhard, Matthew K. Tam
In this work, we consider a continuous dynamical system associated with the fixed point set of a nonexpansive operator which was originally studied by Boţ & Csetnek (2015). Our mai…
Explicit Formula for Preimages of Relaxed One-Sided Lipschitz Mappings with Negative Lipschitz Constants
Andrew C. Eberhard, Boris S. Mordukhovich, Janosch Rieger
This paper addresses Lipschitzian stability issues that play an important role in both theoretical and numerical aspects of variational analysis, optimization, and their applicatio…
Combining Progressive Hedging with a Frank-Wolfe Method to Compute Lagrangian Dual Bounds in Stochastic Mixed-Integer Programming
Natashia Boland, Jeffrey Christiansen, Brian Dandurand +3
We present a new primal-dual algorithm for computing the value of the Lagrangian dual of a stochastic mixed-integer program (SMIP) formed by relaxing its nonanticipativity constrai…