Primal heuristics play a crucial role in exact solvers for Mixed Integer Programming (MIP).
While solvers are guaranteed to find optimal solutions given sufficient time, real-world …

Mixed Integer Programming (MIP) solvers rely on an array of sophisticated heuristics
developed with decades of research to solve large-scale MIP instances encountered in …

The design of strategies for branching in Mixed Integer Programming (MIP) is guided by
cycles of parameter tuning and offline experimentation on an extremely heterogeneous …

Abstract Machine learning (ML) has been recently introduced to solving optimization
problems, especially for combinatorial optimization (CO) tasks. In this paper, we survey the …

Abstract “Primal heuristics” are a key contributor to the improved performance of exact
branch-and-bound solvers for combinatorial optimization and integer programming. Perhaps …

Mixed-integer programming (MIP) technology offers a generic way of formulating and
solving combinatorial optimization problems. While generally reliable, state-of-the-art MIP …

Branch-and-bound is a widely used method in combinatorial optimization, including mixed
integer programming, structured prediction and MAP inference. While most work has been …

Abstract Mixed Integer Programming (MIP) is one of the most widely used modeling
techniques for combinatorial optimization problems. In many applications, a similar MIP …

Cutting planes (cuts) are important for solving mixed-integer linear programs (MILPs), which
formulate a wide range of important real-world applications. Cut selection--which aims to …

State-of-the-art mixed integer programming (MIP) solvers are highly parameterized. For
heterogeneous and a priori unknown instance distributions, no single parameter …