Learn how to design a branch-and-bound algorithm for an integer programming problem in four steps: relax, branch, bound, and repeat. Discover tips and tricks to improve your algorithm.

In this paper, we extend genetic algorithms with double strings based on reference solution updating proposed by Sakawa et al. (1997, 1999) for 0-1 programming problems into integer programming ...

Algorithms for integer programming often take a first stab at a solution with linear programming, which is outwardly similar but allows the variables to vary continuously. Linear constraints, ...

ILP-Based Timetabling: Efficient optimization of timetables using Integer Linear Programming. Hybrid Approaches: Combination of ILP with Genetic Algorithms and Simulated Annealing to improve ...

In this repository, you'll find theoretical materials and code related to my exploration of Quantum algorithms for Integer Programming. Upon graduation, I plan to upload presentation slides and my ...

Research areas: Healthcare optimization under uncertainty, Large-scale optimization, stochastic programming, decomposition-based integer programming algorithms (Benders decomposition, Lagrangian ...

A recent approach, based on integer programming, resolves this tension for non-negative tensor completion. It achieves the information-theoretic sample complexity rate and deploys the blended ...

Integer programming (IP) is a powerful tool used to solve optimization problems with discrete variables. This means the variables can only take on whole number values, representing real-world ...

In this paper, we extend genetic algorithms with double strings based on reference solution updating proposed by Sakawa et al. (1997, 1999) for 0-1 programming problems into integer programming ...