Riemannian gradient descent

Related lectures (32)

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Covers the Lipschitz gradient theorem and its applications in function optimization.

Explores linear recurrence relations, including examples like the Fibonacci numbers and the proof of related theorems.

Focuses on Theorem 2 for functions of class Cn and their Taylor expansion around a.

Covers the basics of constrained optimization, including tangent directions, trust-region subproblems, and necessary optimality conditions.

Explores the connection between Riemannian and Euclidean gradients and Hessians at critical points.

Covers functions extension and Taylor expansion, exploring mathematical concepts in depth.

Introduces linear algebra, calculus, and optimization basics in Euclidean spaces, emphasizing the power of optimization as a modeling tool.

Covers the basic properties of holomorphic maps and Taylor series expansions in complex analysis.

Covers the application of Taylor's formula, including composition of functions and detecting local extrema.

Explores practical aspects of solving parity games, including winning strategies, algorithms, complexity, determinism, and heuristic approaches.