Pseudoconvex function

Related lectures (30)

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Explores the properties and optimization of convex functions, including inequalities and gradient descent.

Introduces optimization basics, covering logistic regression, derivatives, convex functions, gradient descent, and second-order methods.

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

Discusses conditions for functions to be strictly increasing or decreasing based on the derivative.

Explores the sufficient optimality conditions for local minima in functions.

Covers the Gradient Descent method, smoothness in functions, and proofs related to convexity.

Explores stochastic gradient descent optimization and the Mean-Field Method in neural networks.

Explores the role of computation in optimization, focusing on gradient descent for convex and nonconvex problems.

Explores optimal transport, emphasizing convexity properties and inequalities in compact sets.

Discusses interpreting convergence plots in optimization, identifying bugs based on convergence rates, and understanding optimization algorithms.