Lipschitz Gradient Theorem

About
Privacy
Disclaimer

Graph Chatbot

Related lectures (28)

Page 1 of 3

Calculus of Variations: Gradient Young Theorem

Covers the Gradient Young Theorem in the calculus of variations, discussing proofs and applications.

Optimization Methods: Convergence and Trade-offs

Covers optimization methods, convergence guarantees, trade-offs, and variance reduction techniques in numerical optimization.

Extreme Values and Constraints

Explores extreme values, constraints, Riemann's integral interpretation, and volume calculations of parallelipipeds in mathematics.

The Cauchy Theorem for Sequences

Explores the Cauchy theorem for sequences, highlighting the importance of convergence and boundedness.

Non-Convex Optimization: Techniques and Applications

Covers non-convex optimization techniques and their applications in machine learning.

Deep Learning Building Blocks

Covers tensors, loss functions, autograd, and convolutional layers in deep learning.

Local Extrema of Functions

Discusses local extrema of functions in two variables around the point (0,0).

Implicit Functions Theorem

Covers the Implicit Functions Theorem, explaining how equations can define functions implicitly.

Gradient Descent Methods: Theory and Computation

Explores gradient descent methods for smooth convex and non-convex problems, covering iterative strategies, convergence rates, and challenges in optimization.

Primal-dual Optimization: Extra-Gradient Method

Explores the Extra-Gradient method for Primal-dual optimization, covering nonconvex-concave problems, convergence rates, and practical performance.