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Related lectures (31)

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Introduction to Optimization and Operations Research

Covers fundamental concepts of optimization and operations research, exploring real-world examples and key topics over a semester.

Optimization Techniques: Convexity in Machine Learning

Covers optimization techniques in machine learning, focusing on convexity and its implications for efficient problem-solving.

Linear Programming: Weighted Bipartite Matching

Covers linear programming, weighted bipartite matching, and vertex cover problems in optimization.

Convex Optimization: Self-dual Cones

Explores self-dual cones in convex optimization and their applications in various optimization problems.

Optimization Problems: Greedy Algorithms

Explores optimization problems and greedy algorithms to find the best solutions efficiently.

Energy System Modelling: Overview and Optimization Problems

Covers energy system modelling, optimization, scenarios, predictions, complexities, and controversies in energy models.

Dynamic Programming: Fibonacci Numbers

Covers dynamic programming with a focus on Fibonacci numbers and efficient calculation algorithms.

Simplex Method: Basics and Applications

Covers the basics of the Simplex method and its application in solving optimization problems.

Primal-dual optimization: Theory and Computation

Explores primal-dual optimization, conjugation of functions, strong duality, and quadratic penalty methods in data mathematics.

Lagrangian Duality: Convex Optimization

Explores Lagrangian duality in convex optimization, transforming problems into min-max formulations and discussing the significance of dual solutions.