Strong dual space

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Explores bases, dual spaces, and linear applications in linear algebra.

Covers basics on optimization, including norms, Lipschitz continuity, and convexity concepts.

Covers weak and strong duality in optimization problems, focusing on Lagrange multipliers and KKT conditions.

Explores minimax optimization theory, including weak and strong duality, saddle points, and practical algorithm performance.

Covers Lagrangian, dual function, dual problem, weak duality, and optimality.

Covers matrix operations and their applications in various fields.

Covers faster gradient descent methods and projected gradient descent for constrained optimization in machine learning.

Explores optimization duality, weak and strong duality, practical optimization algorithms, and challenges in nonconvex-concave problems.

Explores the convergence proof of Stochastic Gradient Descent with strongly-convex functions and the impact of step-size on convergence properties.

Explores convexity, Jacobians, subdifferentials, and convergence rates in optimization and function analysis.

Discusses advanced optimization techniques, focusing on faster and projected gradient descent methods in machine learning.

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