Skip to main content
Graph
Search
fr
en
Login
Search
All
Categories
Concepts
Courses
Lectures
MOOCs
People
Practice
Publications
Startups
Units
Show all results for
Home
Lecture
Lagrangian Duality: Convex Optimization
Graph Chatbot
Related lectures (29)
Previous
Page 3 of 3
Next
Linear Optimization: Finding Initial BFS
Explains the process of finding an initial Basic Feasible Solution for linear optimization problems using the Simplex Algorithm.
KKT and Convex Optimization
Covers the KKT conditions and convex optimization, discussing constraint qualifications and tangent cones of convex sets.
Stochastic Gradient Descent: Optimization and Convergence
Explores stochastic gradient descent, covering convergence rates, acceleration, and practical applications in optimization problems.
Duality in Linear Programming
Explores the concept of duality in linear programming, discussing the relationship between primal and dual problems.
Linear Optimization: Auxiliary Problem
Explores the formulation of the auxiliary problem in linear optimization and its role in optimal decision-making.
Convex Sets: Mathematical Optimization
Introduces convex optimization, covering convex sets, solution concepts, and efficient numerical methods in mathematical optimization.
Optimization Principles
Covers optimization principles, including linear optimization, networks, and concrete research examples in transportation.
Optimization Techniques: Convexity in Machine Learning
Covers optimization techniques in machine learning, focusing on convexity and its implications for efficient problem-solving.
Quantile Regression: Linear Optimization
Covers quantile regression, focusing on linear optimization for predicting outputs and discussing sensitivity to outliers, problem formulation, and practical implementation.