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

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 …

State-of-the-art solvers for mixed integer programming (MIP) problems are highly
parameterized, and finding parameter settings that achieve high performance for specific …

Cutting plane methods play a significant role in modern solvers for tackling mixed-integer
programming (MIP) problems. Proper selection of cuts would remove infeasible solutions 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 …

The best-performing algorithms for many hard problems are highly parameterized. Selecting
the best heuristics and tuning their parameters for optimal overall performance is often a …

We report on the selection process leading to the sixth version of the Mixed Integer
Programming Library, MIPLIB 2017. Selected from an initial pool of 5721 instances, the new …

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 identification of performance-optimizing parameter settings is an important part of the
development and application of algorithms. We describe an automatic framework for this …