Optimization Techniques: Linear Programming to Reinforcement Learning - Part 2 скачать в хорошем качестве

Optimization Techniques: Linear Programming to Reinforcement Learning - Part 2

About the Talk 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 essentials: objectives, constraints, feasibility regions, and that sweet spot where everything aligns—the optimal solution. Once the theory’s in place, we’ll dive into real-world solving techniques: Linear Programming (LP) for structure and precision Genetic Algorithms (GA) for adaptive problem-solving Reinforcement Learning (RL) for environments where learning matters Whether you're into clean equations or chaotic simulations, this talk will give you a practical view of how different optimization methods work, and where they shine in real-life scenarios. About the Speaker Dr. Karthika Vijayan Solution Consultant, Sahaj Software Dr. Karthika focuses on crafting customized AI solutions to solve real-world business problems. She specializes in designing composite machine learning pipelines and has worked extensively in the field of conversational AI with voice and text data for over a decade. Her approach blends deep technical expertise with a strong focus on business impact. Sujit Kamthe Solution Consultant, Sahaj Software Sujit is a technology leader with over a decade of experience in building high-performance, distributed systems. A full-stack polyglot developer, he specializes in Functional Programming, Microservices, Data Engineering, and DevOps. He has led numerous complex data and optimization projects and is passionate about scaling systems, mentoring teams, and transforming problems into elegant, engineered solutions. 🎥 Watch the full talk to explore how structured logic, evolutionary thinking, and adaptive learning come together to solve complex optimization problems.