Optimization Techniques: Linear Programming to Reinforcement Learning

Optimization is at the heart of smart decision-making, whether you're allocating resources, planning routes, or just trying to get the most toppings on your pizza without breaking the budget. In this talk, we'll explore what makes an optimization problem tick. We'll break down the core components: objectives, constraints, feasibility regions, and the sweet spot where everything aligns to deliver what is known as the optimal solution. Once we’ve got a grip on the theory, we’ll roll up our sleeves and look at how to solve constrained optimization problems using a mix of methods—Linear Programming (LP) for its structured elegance, Genetic Algorithms (GA) for their evolutionary flair, and Reinforcement Learning (RL) for a more adaptive, learning-based approach. Each method offers a unique lens on how to navigate constraints and complexity. Whether you’re a fan of clean equations or chaotic simulations, you’ll leave with a clearer understanding of the practical aspects of optimization techniques with real world examples.