Gaussian Process-Based Model Predictive Control

Related lectures (30)

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Covers various problem-solving techniques and their real-world applications.

Explores energy system optimization, including heat, cost, and CO2 emissions.

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

Explores degree of freedom analysis, redundancy, and data reconciliation in process modeling and optimization.

Explores the concept of duality in linear programming and its practical implications in optimization.

Covers optimization techniques, quizzes, and grade exercises in mathematical applications.

Explores the link between linear systems and optimization through elimination and LU decomposition.

Explains how to solve linear equations using the least squares method to minimize errors in the system.

Delves into the theory and optimization of Support Vector Machines, including Mercer's Theorem and Lagrange Duality.

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