Explore mathematical optimization techniques for AI problem-solving in this comprehensive conference talk. Dive into constraint-based optimization, feasibility vs. optimization, and various problem classes. Learn to define optimization models using decision variables, constraints, and objectives. Examine real-world examples like Sudoku and scheduling problems. Gain practical insights into implementing these concepts using Google OR-Tools, with a focus on code implementation rather than complex mathematics. Discover how to efficiently solve problems with multiple solutions in AI systems, including capacity utilization, shortest path finding, and optimal scheduling. Perfect for software developers looking to enhance their AI development skills with optimization techniques.