Primal-dual Optimization: Extra-Gradient Method

Related lectures (27)

Page 2 of 3

Explores algorithms for composite optimization, including proximal operators and gradient methods, with examples and theoretical bounds.

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

Explores implicit bias, gradient descent, stability in optimization algorithms, and generalization bounds in machine learning.

Explores generalization in deep learning, covering model complexity, implicit bias, and the double descent phenomenon.

Explores the role of computation in data mathematics, focusing on iterative methods, optimization, estimators, and descent principles.

Explores linear systems, convergence, and solving methods with a focus on CPU time and memory requirements.

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

Covers the optimality of convergence rates in accelerated and stochastic gradient descent methods for non-convex optimization problems.

Explores stochastic gradient descent, covering convergence rates, acceleration, and practical applications in optimization problems.

Explores primal-dual optimization methods, emphasizing Lagrangian gradient techniques and their applications in data optimization.