Primal heuristics are important for solving mixed integer linear programs, because they find
feasible solutions that facilitate branch and bound search. A prominent group of primal …

Abstract Integer Linear Programs (ILPs) are powerful tools for modeling and solving a large
number of combinatorial optimization problems. Recently, it has been shown that Large …

We present an integrated prediction-optimization (PredOpt) framework to efficiently solve
sequential decision-making problems by predicting the values of binary decision variables …

Abstract Large Neighborhood Search (LNS) heuristics are among the most powerful but also
most expensive heuristics for mixed integer programs (MIP). Ideally, a solver adaptively …

Finding high-quality solutions to mixed-integer linear programming problems (MILPs) is of
great importance for many practical applications. In this respect, the refinement heuristic …

We present Undercover, a primal heuristic for nonconvex mixed-integer nonlinear programs
(MINLPs) that explores a mixed-integer linear subproblem (sub-MIP) of a given MINLP. We …

Nogood learning has proven to be an effective CSP technique critical to success in today's
top SAT solvers. We extend the technique for use in integer programming and mixed integer …

Cutting-plane methods have enabled remarkable successes in integer programming over
the last few decades. State-of-the-art solvers integrate a myriad of cutting-plane techniques …

We propose a deep reinforcement learning (RL) method to learn large neighborhood search
(LNS) policy for integer programming (IP). The RL policy is trained as the destroy operator to …

In this paper, we identify some of the computational advances that have been contributing to
the efficient solution of mixed-integer linear programming (MILP) problems. Recent features …