Mixing Space I

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Optimization with Constraints: KKT Conditions

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

Linear Programming: Weighted Bipartite Matching

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

Optimization with Constraints: KKT Conditions

Covers the optimization with constraints, focusing on the Karush-Kuhn-Tucker (KKT) conditions.

Energy Efficiency in Buildings

Explores energy efficiency in buildings, emphasizing the importance of reducing energy consumption while maintaining service levels.

Optimization: Gradient Descent and Subgradients

Explores optimization methods like gradient descent and subgradients for training machine learning models, including advanced techniques like Adam optimization.

Simplex algorithm: From vertex to vertex

Explains the simplex algorithm process of finding the next vertex and selecting variables.

Linear Models for Classification: Logistic Regression and SVM

Covers linear models for classification, focusing on logistic regression and support vector machines.

Proximal gradient descent and intro linear models

Covers proximal gradient descent and linear models in regression analysis.

Optimization Methods: Theory Discussion

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

Decisions under Risk and Uncertainty: Behavioral Economics Insights

Delves into decision-making under risk, bounded rationality, prospect theory, and framing, with insights from behavioral economics.