Discrete Optimization: Practical Solving Algorithms

In many industries, finding the most efficient way to allocate resources, route vehicles, or schedule tasks can save millions. Discrete optimization provides the mathematical tools to solve these complex decision-making problems systematically. This course guides you through the foundational concepts of discrete optimization, showing you how to formulate real-world problems mathematically and solve them using powerful algorithmic techniques. You will transition from understanding basic search spaces to implementing modern heuristics and solver-based solutions. What you'll learn: 1. Understand core concepts of discrete optimization, including integer programming and combinatorial search spaces. 2. Apply branch-and-bound and branch-and-cut algorithms to find exact optimal solutions. 3. Formulate complex scheduling and routing problems using modern Python-based modeling libraries. 4. Implement heuristic and metaheuristic approaches, such as local search, for large-scale problems. 5. Analyze algorithmic complexity and evaluate the performance of different solver engines. 6. Configure constraint programming models for highly restricted decision problems. We begin with foundational definitions, terminology, and formulation techniques before moving on to exact algorithms, heuristics, and modern programmatic solvers. You will read clear explanations, study structured code examples, and practice through written optimization exercises. This course is designed for beginners in operations research, computer science, or data analysis looking to build a strong foundation in optimization. No advanced mathematical background is required. Start mastering the algorithms that power modern logistics and decision-making systems today.