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Networks: Formulation
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Related lectures (48)
Linear Programming: Solving LPs
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Covers the process of solving Linear Programs (LPs) using the simplex method.
Optimization Techniques: Gradient Descent and Convex Functions
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Provides an overview of optimization techniques, focusing on gradient descent and properties of convex functions in machine learning.
Duality: Economic Interpretation
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Explores duality in linear programming, strong duality, complementary slackness, and the economic interpretation of dual variables as prices.
Portfolio Optimization: Models and Strategies
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Explores portfolio optimization models and strategies under uncertainty, emphasizing decision criteria like value-at-risk and mean-variance functional.
Convex Optimization Problems: Standard Form
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Covers convex optimization problems, transformation to standard form, and optimality criteria for differentiable objectives.
Initial BFS: Finding Solutions
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Covers the concept of finding an initial BFS and solving related optimization problems.
Faster Gradient Descent: Projected Optimization Techniques
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Covers faster gradient descent methods and projected gradient descent for constrained optimization in machine learning.
Proximal Gradient Descent: Optimization Techniques in Machine Learning
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Discusses proximal gradient descent and its applications in optimizing machine learning algorithms.
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