Lecture

Convex Functions: Theory and Applications

Related lectures (32)

Explores convex functions, affine transformations, pointwise maximum, minimization, Schur's Lemma, and relative entropy in mathematical optimization.

Convex Optimization: Theory and Applications

Explores the theory and applications of convex optimization, covering topics such as log-determinant function, affine transformations, and relative entropy.

Convex Optimization: Convex Functions

Covers the concept of convex functions and their applications in optimization problems.

Convex Functions

Covers the properties and operations of convex functions.

Convex Optimization: Elementary Results

Explores elementary results in convex optimization, including affine, convex, and conic hulls, proper cones, and convex functions.

Convex Functions: Theory and Applications

Introduces convex functions, covering affine, convex, and conic hulls, transformations, inequalities, and conditions for convexity.

Geodesic Convexity: Theory and Applications

Explores geodesic convexity in metric spaces and its applications, discussing properties and the stability of inequalities.

Convex Optimization: Notation and Matrix Norms

Introduces Convex Optimization notation, convex functions, vector norms, and matrix properties.

Linear Algebra Review: Convex Optimization

Covers essential linear algebra concepts for convex optimization, including vector norms, eigenvalue decomposition, and matrix properties.

Convex Sets and Functions

Introduces convex sets and functions, discussing minimizers, optimality conditions, and characterizations, along with examples and key inequalities.

Mathematics of Data: Optimization Basics

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

Optimal Transport: Rockafellar Theorem

Explores the Rockafellar Theorem in optimal transport, focusing on c-cyclical monotonicity and convex functions.

Convex Optimization

Covers an overview of convex optimization, affine sets, polyhedra, ellipsoids, and convex functions.

Convex Functions: Elementary Concepts

Introduces elementary concepts of convex functions, including hulls, transformations, conditions, and examples.

Convex Optimization: Introduction and Sets

Covers the fundamentals of convex optimization, including mathematical problems, minimizers, and solution concepts, with an emphasis on efficient methods and practical applications.

Convex Sets: MGT-418 Lecture

On Convex Optimization covers course organization, mathematical optimization problems, solution concepts, and optimization methods.

Information Theory: Channel Capacity and Convex Functions

Explores channel capacity and convex functions in information theory, emphasizing the importance of convexity.

Convex Sets: Mathematical Optimization

Introduces convex optimization, covering convex sets, solution concepts, and efficient numerical methods in mathematical optimization.

Convexity of Sum of Inverse Eigenvalues

Covers the convexity proof of the sum of the eigenvalues of the inverse matrix.

Galilean Transformations: Spacetime and Measurements

Explores Galilean transformations in spacetime, focusing on measurements and coordinate transformations.