Lectures related to Logarithmically concave function

Covers the discussion of local extrema, concavity, convexity, and inflection points in functions.

Covers the optimization of volume by constructing a rectangular box.

Explores phase coexistence, gas stability, and entropy in relation to the Van der Waals equation and phase diagrams.

Covers the concept of log-concave functions and their implications in probability distributions and Gaussian correlation inequalities.

Covers the concepts of limits and continuity, focusing on the demonstration of various theorems.

Covers implicit curves, regular and critical points, convexity, concavity, and inflection points.

Explores concavity, convexity, critical points, and singularities in functions.

Explores applications of differential calculus, including theorems, convexity, extrema, and inflection points.

Explains the concept of convexity in optimization and its importance in finding global optima.

Covers the formation theory and coding, focusing on channel capacity and concave functions.

Explores convexity, concavity, and limiting developments for functions, emphasizing extrema and derivative properties.

Explores inflection points, convexity, concavity, and asymptotes in functions, with examples and applications.

Discusses the theorems on convex functions and provides examples for better understanding.

Covers advanced topics on the limitations of concave and convex shapes.

Explores Gaussian corelation inequality, Anderson's theorem, and log-concavity in probability theory.

Covers concepts of entropy, Gaussian distributions, and channel capacity with constraints.

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

Covers the physics of a toboggan jump, analyzing forces, speeds, and motion equations.

Explores primal-dual optimization algorithms for convex-concave minimax problems, discussing convergence properties and applications.

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