Convex Optimization: Exercises

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Related lectures (30)

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

Numerical Methods: Stopping Criteria, SciPy, and Matplotlib

Discusses numerical methods, focusing on stopping criteria, SciPy for optimization, and data visualization with Matplotlib.

Convex Optimization Problems

Covers Convex Optimization Problems, LP formulations, and practical implementations using CVXPY and GUROBI.

KKT and Convex Optimization

Covers the KKT conditions and convex optimization, discussing constraint qualifications and tangent cones of convex sets.

Legendre Transform

Explores the Legendre transform, duality in convex analysis, and optimization problems.

Calculating with Taylor, Convexity

Covers calculating with Taylor series and convexity concepts, including sin(x) and cos(x) approximations.

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