Dual norm

Related lectures (11)

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

Explores the Thomas algorithm for tridiagonal systems and the accuracy of direct methods in numerical computations.

Introduces Fenchel conjugation, exploring its properties, examples, and applications in nonsmooth optimization problems and minimax formulations.

Explores linear programming duality, covering constraints, variables, solutions, and the relationship between primal and dual LP.

Covers the weak formulation of elliptic partial differential equations and the uniqueness of solutions in Hilbert space.

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

Explores constrained convex optimization with min-max formulation and Fenchel conjugation.

Covers the local inversion theorem and uniqueness of solutions in a ball around a point.

Explores Mirror Prox optimization, argmin function, norms, and their practical applications in optimization problems.

Explores weak derivatives in Sobolev spaces, discussing their properties and uniqueness.

Covers matrix norms, including the spectral and Frobenius norms.

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