Explore the algorithms and solvers that power mathematical optimization, understanding their strengths, limitations, and appropriate use cases.
Understand the algorithms behind linear programming solvers like Simplex and Interior Point methods.
Explore branch-and-bound, cutting planes, and other techniques for solving integer programming problems.
Learn about genetic algorithms, simulated annealing, and other heuristic approaches for complex optimization problems.
Discover how quantum-inspired algorithms can be applied to solve challenging optimization problems.
Learn how to choose the right solver for different types of optimization problems based on problem characteristics.
Compare the performance of different solvers across various problem types and sizes.
Each algorithm guide in this collection includes: