EE-Learning
One Dimensional Optimization – Optimality Conditions
Catogry:
Computing
Subject:
Computer Science
Course:
Numerical Optimization
Lecture List
Summary
Barrier and Penalty Methods, Augmented Lagrangian Method and Cutting Plane Method
Active Set Method (contd)
Lagrange Methods, Active Set Method
Karmarkar’s Method
Interior Point Methods – Affine Scaling Method
Duality in Linear Programming
Simplex Algorithm and Two-Phase Method
Optimality Conditions and Simplex Tableau
Basic Feasible Solution
Geometric Solution
Linear Programming Problem
Lagrangian Saddle Point and Wolfe Dual
Geometric Interpretation
Weak and Strong Duality
Second Order KKT Conditions (contd)
Second Order KKT Conditions
Convex Programming Problem IV
Constraint Qualifications
First Order KKT Conditions
Feasible and Descent Directions
Constrained Optimization – Local and Global Solutions, Conceptual Algorithm
Quasi-Newton Methods – Rank One Correction, DFP Method II
Conjugate Directions
Quasi-Newton Methods – Rank One Correction, DFP Method I
Quasi-Newton Methods – Rank One Correction, DFP Method
Trust Region and Quasi-Newton Methods
Classical Newton Method
Steepest Descent Method
Global Convergence Theorem
Line Search Techniques
Multi Dimensional Optimization – Optimality Conditions, Conceptual Algorithm
Convex Functions (contd) III
Convex Functions II
Convex Sets (contd) I
Convex Sets
One Dimensional Optimization – Optimality Conditions
Mathematical Background (contd) I
Mathematical Background
Introduction