Skip to main content
Graph
Search
fr
en
Login
Search
All
Categories
Concepts
Courses
Lectures
MOOCs
People
Quizes
Exercises
Publications
Startups
Units
Show all results for
Home
Lecture
Polynomial Optimization: SOS and SDP
Graph Chatbot
Related lectures (40)
Convex Optimization: Generalized Inequalities
Log in to Mediaspace to watch this video
Explores problems with generalized inequalities in convex optimization and the equivalence between SOCP and SDP.
Lagrangian Duality: Theory and Applications
Log in to Mediaspace to watch this video
Explores Lagrangian duality in convex optimization, discussing strong duality, dual solutions, and practical applications in second-order cone programs.
Convex Optimization
Log in to Mediaspace to watch this video
Introduces convex optimization, focusing on the importance of convexity in algorithms and optimization problems.
Convex Optimization: Gradient Algorithms
Log in to Mediaspace to watch this video
Covers convex optimization problems and gradient-based algorithms to find the global minimum.
Semi-Definite Programming
Log in to Mediaspace to watch this video
Covers semi-definite programming and optimization over positive semidefinite cones.
Legendre Transform
Log in to Mediaspace to watch this video
Explores the Legendre transform, duality in convex analysis, and optimization problems.
Optimization Problems: Path Finding and Portfolio Allocation
Log in to Mediaspace to watch this video
Covers optimization problems in path finding and portfolio allocation.
Faster Gradient Descent: Projected Optimization Techniques
Log in to Mediaspace to watch this video
Covers faster gradient descent methods and projected gradient descent for constrained optimization in machine learning.
Convexifying Nonconvex Problems: SVM and Dimensionality Reduction
Log in to Mediaspace to watch this video
Explores convexifying nonconvex problems through SVM and dimensionality reduction techniques.
Convex Optimization Problems: Standard Form
Log in to Mediaspace to watch this video
Covers convex optimization problems, transformation to standard form, and optimality criteria for differentiable objectives.
Optimization Techniques: Gradient Descent and Convex Functions
Log in to Mediaspace to watch this video
Provides an overview of optimization techniques, focusing on gradient descent and properties of convex functions in machine learning.
The Geometry of Linear Optimization
Log in to Mediaspace to watch this video
Delves into linear optimization formulation, capacity expansion, investment under taxation, and revenue management in various industries.
Proximal Gradient Descent: Optimization Techniques in Machine Learning
Log in to Mediaspace to watch this video
Discusses proximal gradient descent and its applications in optimizing machine learning algorithms.
Two-phase Simplex Algorithm: Introduction and Duality
Log in to Mediaspace to watch this video
Introduces the two-phase simplex algorithm and explores duality in linear programming.
Portfolio Optimization: Models and Strategies
Log in to Mediaspace to watch this video
Explores portfolio optimization models and strategies under uncertainty, emphasizing decision criteria like value-at-risk and mean-variance functional.
Optimization Principles
Log in to Mediaspace to watch this video
Covers optimization principles, including linear optimization, networks, and concrete research examples in transportation.
Optimization Programs: Piecewise Linear Cost Functions
Log in to Mediaspace to watch this video
Covers the formulation of optimization programs for minimizing piecewise linear cost functions.
Linear Optimization: Fundamentals
Log in to Mediaspace to watch this video
Covers the basics of linear optimization, including equations, polyhedrons, feasible directions, and optimal solutions.
Equality and Inequality Constraints: Optimization Conditions
Log in to Mediaspace to watch this video
Covers necessary optimality conditions for optimization with constraints and discusses cones and polar sets.
Convex Relaxation: Negative Type Theorems
Log in to Mediaspace to watch this video
Explores convex relaxation and negative type theorems in convex programs.
Previous
Page 2 of 2
Next