Optimization and Simulation

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Related lectures (31)

Page 1 of 4

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

Markov Chains: Simulation and Optimization

Explores Markov chains, Metropolis-Hastings, and simulation for optimization purposes, highlighting the significance of ergodicity in efficient variable simulation.

Markov Chains and Algorithm Applications

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

Simulation & Optimization: Poisson Process & Random Numbers

Explores simulation pitfalls, random numbers, discrete & continuous distributions, and Monte-Carlo integration.

Optimization with Constraints: KKT Conditions

Covers the KKT conditions for optimization with constraints, essential for solving constrained optimization problems efficiently.

Optimization and Simulation: Bayesian Inference

Explores Bayesian inference, knapsack problem, and prediction using Markov Chain Monte Carlo methods.

Optimization Methods: Theory Discussion

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

Monte Carlo Chain: Motivation and Algorithm

Explores the motivation and algorithm behind the Monte Carlo Chain method.

Metropolis Algorithm: Optimization and Probabilistic Approach

Covers the Metropolis algorithm for approximate optimization using lazy chains and exploring state space efficiently.

Energy Systems Optimization

Explores energy systems modeling, optimization, and cost analysis for efficient operations.