Metropolis Algorithm: Optimization and Probabilistic Approach

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Optimization Methods: Theory Discussion

Explores optimization methods, including unconstrained problems, linear programming, and heuristic approaches.

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Optimization and Simulation

Explores optimization techniques like Metropolis-Hastings and Simulated Annealing through Markov chains and stationary distributions.

Optimization and Simulation

Covers optimization and simulation techniques for drawing from multivariate distributions and dealing with correlations.

Traveling Salesman Problem: Introduction and Approximation Methods

Introduces the Traveling Salesman Problem and explores approximation methods using Markov chains.

Introduction to Optimization and Operations Research

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Markov Chains and Algorithm Applications

Covers the application of Markov chains and algorithms for function optimization and graph colorings.

Introduction to Optimization

Covers the basics of optimization, including historical perspectives, mathematical formulations, and practical applications in decision-making problems.

Optimization Techniques: Convexity in Machine Learning

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MILP Model and Typical Days by FM

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