Optimization problems

Related lectures (24)

Page 2 of 3

Explores extreme values, constraints, Riemann's integral interpretation, and volume calculations of parallelipipeds in mathematics.

Covers the definition of convex functions and their properties in optimization.

Explores trigonometric derivatives, composition of functions, and inflection points in differential calculation.

Covers advanced optimization techniques using Lagrange multipliers to find extrema of functions subject to constraints.

Explores techniques for minimizing functions and finding critical points.

Explores primal-dual optimization methods, emphasizing Lagrangian gradient techniques and their applications in data optimization.

Covers the optimization of functions, focusing on finding the maximum and minimum values.

Explores geodesic convexity, focusing on properties of convex functions on manifolds.

Covers non-convex optimization techniques and their applications in machine learning.

Explores convexity, optimization, and critical points in mathematical functions using the second derivative.