Lectures related to Convex Optimization: Exercises

Introduces geodesic convexity on Riemannian manifolds and explores its properties.

Linear Programming: Weighted Bipartite Matching

Covers linear programming, weighted bipartite matching, and vertex cover problems in optimization.

Explores convexity, Jacobians, subdifferentials, and convergence rates in optimization and function analysis.

Optimization Methods: Convergence and Trade-offs

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

Primal-dual Optimization: Extra-Gradient Method

Explores the Extra-Gradient method for Primal-dual optimization, covering nonconvex-concave problems, convergence rates, and practical performance.

Optimization Methods: Theory Discussion

Explores optimization methods, including unconstrained problems, linear programming, and heuristic approaches.

MATLAB Essentials: Functions and Variables

Covers essential MATLAB functions, variables, loops, and debugging tools.

Primal-dual optimization: Theory and Computation

Explores primal-dual optimization, conjugation of functions, strong duality, and quadratic penalty methods in data mathematics.

Local Extrema of Functions

Discusses local extrema of functions in two variables around the point (0,0).

Optimization with Constraints: KKT Conditions

Covers the KKT conditions for optimization with constraints, essential for solving constrained optimization problems efficiently.

Primal-dual Optimization III: Lagrangian Gradient Methods

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

Convex Functions: Theorems and Examples

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

Optimization Techniques: Convexity in Machine Learning

Covers optimization techniques in machine learning, focusing on convexity and its implications for efficient problem-solving.

Taylor's Formula: Developments and Extrema

Covers Taylor's formula, developments, and extrema of functions, discussing convexity and concavity.

Gradient Descent Methods: Theory and Computation

Explores gradient descent methods for smooth convex and non-convex problems, covering iterative strategies, convergence rates, and challenges in optimization.

Convex Functions Log in to Mediaspace to watch this video

Covers the properties and operations of convex functions.

Convex Optimization: Convex Functions Log in to Mediaspace to watch this video

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

Convex Optimization Tutorial: KKT Conditions Log in to Mediaspace to watch this video

Explores KKT conditions in convex optimization, covering dual problems, logarithmic constraints, least squares, matrix functions, and suboptimality of covering ellipsoids.

Optimization Methods: Lagrange Multipliers Log in to Mediaspace to watch this video

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

Optimization Problems: Path Finding and Portfolio Allocation Log in to Mediaspace to watch this video

Covers optimization problems in path finding and portfolio allocation.