Math 2.0

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A Primer to Mathematical Optimization H9BW2EH318

URIm2://res/H9BW2EH318
Kindcourse
CreatorsDebdas Ghosh (instructor)
Date2023-01-15
Scopein

Expressions

e1: edition

Languageen
Date2023-01-15
Contents
  • part 1: Week 01
  • session 1: Lecture 01: Introduction and Histroy on the Development of Optimization Theory
  • session 2: Lecture 02: Tools from Linear Algebra
  • session 3: Lecture 03: Tools from Linear Algebra
  • part 2: Week 02
  • session 4: Lecture 04 : Tools for calculus of several variables
  • session 5: Lecture 05 : Tools from calculus of several variables
  • part 3: Week 03
  • session 6: Lecture 6: Order of Convergence
  • session 7: Lecture 7: Continuity
  • part 4: Week 04
  • session 8: Lecture 8 & 9 : Partial and Directional Derivatives and Differentiability
  • part 5: Week 05
  • session 9: Lecture - 10 : Taylor's Theorem
  • session 10: Lecture - 11 : Convex Sets & Convexity Preserving Operations
  • session 11: Lecture - 12 : Separation Result
  • part 6: Week 06
  • session 12: Lecture - 13 & 14 : Theorems of Alternatives
  • session 13: Lecture - 15 : Convex Functions
  • session 14: Lecture -16 : Properties and Zeroth Order Characterization of Convex Functions
  • session 15: Lecture - 17 : First-Order and Second-Order Characterization of Convex Functions
  • session 16: Lecture - 18 : Convexity Preserving Operations
  • session 17: Lecture - 19 : Optimality and Coerciveness
  • part 7: Week 07
  • session 18: Lecture 20 part 1: First Order Optimality Condition
  • session 19: Lecture 20 part 2: Second Order Optimality Conditions
  • session 20: Lecture 21: General Structure of Unconstrained Optimization Algorithms
  • session 21: Lecture 22: Inexact Line Search
  • session 22: Lecture 23 & 24: Global Convergence of Descent Methods
  • session 23: Lecture 25: Where do Descent Methods Converge?
  • part 8: Week 08
  • session 24: Lecture - 26 : Scaling Of Variables
  • session 25: Lecture - 27 : Practical Stopping Criteria
  • session 26: Lecture - 28 & 29 : Steepest Descent Method
  • session 27: Lecture - 30, 31 & 32 : Newton's Method
  • part 9: Week 09
  • session 28: Lecture - 33, 34 & 35
  • session 29: Lecture - 36 & 37
  • session 30: Lecture - 38 Part 1
  • session 31: Lecture - 38 Part 2
  • part 10: Week 10
  • session 32: Lecture - 39 : A Revisit to Lagrange Multipliears Method
  • session 33: Lecture - 40 : Special Cones for Contrained Optimization
  • session 34: Lecture - 41 : Tangent Cone
  • session 35: Lecture - 42 & 43 : First-Order KKT Optimality Conditions
  • session 36: Lecture 44 : Second-Order KKT Optimality Conditions
  • session 37: Lecture 45 : Constraint Qualifications
  • part 11: Week 11
  • session 38: Lecture 46, 47, 48, 49 & 50 : Lagrangian Duality Theory
  • session 39: Lecture 51, 52 & 53 : Methods for Linearly Constrained Problems
  • part 12: Week 12
  • session 40: Lecture 54 : Interior-Point Method for QPP
  • session 41: Lecture 55 : Penalty Methods
  • session 42: Lecture 56 : Sequential Quadratic Programming Method

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