Course Modules

Lecture 1: Course intro; Optimization problems; Bisection Search

Lecture 1: Course intro; Optimization problems; Bisection Search
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Lecture 1: Course intro; Optimization problems; Bisection Search 93534    
  • Lecture 1.pdf Attachment
    Lecture 1.pdf Lecture 1.pdf
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Lecture 2: Convexity and the Center of Mass Cutting Plane Method

Lecture 2: Convexity and the Center of Mass Cutting Plane Method
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Lecture 2: Convexity and the Center of Mass Cutting Plane Method 94313    
  • Lecture 2.pdf Attachment
    Lecture 2.pdf Lecture 2.pdf
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Lecture 3: Gradient Descent (including Steepest Descent and Backtracking Line Search)

Lecture 3: Gradient Descent (including Steepest Descent and Backtracking Line Search)
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Lecture 3: Gradient Descent (including Steepest Descent and Backtracking Line Search) 94526    
  • Lecture 3.pdf Attachment
    Lecture 3.pdf Lecture 3.pdf
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Lecture 4: Newton's Method

Lecture 4: Newton's Method
Module Completed Module In Progress Module Locked
Lecture 4: Newton's Method 95007    
  • Lecture 4.pdf Attachment
    Lecture 4.pdf Lecture 4.pdf
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Between GD and Newton: Conjugate Gradient Descent, Quasi Newton, Accelerated Gradient Descent, and Momentum

Between GD and Newton: Conjugate Gradient Descent, Quasi Newton, Accelerated Gradient Descent, and Momentum
Module Completed Module In Progress Module Locked
Between GD and Newton: Conjugate Gradient Descent, Quasi Newton, Accelerated Gradient Descent, and Momentum 97461    
  • Lecture 5.pdf Attachment
    Lecture 5.pdf Lecture 5.pdf
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Lecture 6: Constrained Optimization, Lagrange Duality

Lecture 6: Constrained Optimization, Lagrange Duality
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Lecture 6: Constrained Optimization, Lagrange Duality 97560    
  • Lecture 6.pdf Attachment
    Lecture 6.pdf Lecture 6.pdf
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Lecture 7: KKT Optimality Conditions; Geometric Interpretation of Duality

Lecture 7: KKT Optimality Conditions; Geometric Interpretation of Duality
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Lecture 7: KKT Optimality Conditions; Geometric Interpretation of Duality 97962    
  • Lecture 7.pdf Attachment
    Lecture 7.pdf Lecture 7.pdf
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Lecture 8: From Fenchel Conjugates to Semi-Definite Constraints

Lecture 8: From Fenchel Conjugates to Semi-Definite Constraints
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Lecture 8: From Fenchel Conjugates to Semi-Definite Constraints 98092    
  • Lecture 8.pdf Attachment
    Lecture 8.pdf Lecture 8.pdf
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Lecture 9: Matrix Inequalities and Semi-Definite Programming

Lecture 9: Matrix Inequalities and Semi-Definite Programming
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Lecture 9: Matrix Inequalities and Semi-Definite Programming 98337    
  • Lecture 9.pdf Attachment
    Lecture 9.pdf Lecture 9.pdf
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Lecture 10: Generalized Inequalities and Multiple Objectives

Lecture 10: Generalized Inequalities and Multiple Objectives
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Lecture 10: Generalized Inequalities and Multiple Objectives 98627    
  • Lecture 10.pdf Attachment
    Lecture 10.pdf Lecture 10.pdf
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Lecture 11: Equality Constraint Newton, Log Barriers and the Central Path

Lecture 11: Equality Constraint Newton, Log Barriers and the Central Path
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Lecture 11: Equality Constraint Newton, Log Barriers and the Central Path 98733    
  • Lecture 11.pdf Attachment
    Lecture 11.pdf Lecture 11.pdf
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Lecture 12: Interior Point Methods

Lecture 12: Interior Point Methods
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Lecture 12: Interior Point Methods 98833    
  • Lecture 12.pdf Attachment
    Lecture 12.pdf Lecture 12.pdf
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Lecture 13: Primal Dual Methods; The Simplex Method

Lecture 13: Primal Dual Methods; The Simplex Method
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Lecture 13: Primal Dual Methods; The Simplex Method 99063    
  • Lecture 13.pdf Attachment
    Lecture 13.pdf Lecture 13.pdf
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Lecture 14: The Ellipsoid Algorithm

Lecture 14: The Ellipsoid Algorithm
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Lecture 14: The Ellipsoid Algorithm 99180    
  • Lecture 14.pdf Attachment
    Lecture 14.pdf Lecture 14.pdf
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Lecture 15: Gradient Descent with Constraints; Lower Bounds

Lecture 15: Gradient Descent with Constraints; Lower Bounds
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Lecture 15: Gradient Descent with Constraints; Lower Bounds 99491    
  • Lecture 15.pdf Attachment
    Lecture 15.pdf Lecture 15.pdf
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Lecture 16: Lower Bounds (continued); Stochastic Methods

Lecture 16: Lower Bounds (continued); Stochastic Methods
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Lecture 16: Lower Bounds (continued); Stochastic Methods 100136    
  • Lecture 16.pdf Attachment
    Lecture 16.pdf Lecture 16.pdf
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Lecture 17: Prox Methods; Mirror Descent

Lecture 17: Prox Methods; Mirror Descent
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Lecture 17: Prox Methods; Mirror Descent 100198    
  • Lecture 17.pdf Attachment
    Lecture 17.pdf Lecture 17.pdf
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Lecture 18: Summary

Lecture 18: Summary
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Lecture 18: Summary 100714    
  • Lecture 18.pdf Attachment
    Lecture 18.pdf Lecture 18.pdf
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Supplemental Problems

Supplemental Problems
Module Completed Module In Progress Module Locked
Supplemental Problems 100708    
  • supp.pdf Attachment
    supp.pdf supp.pdf
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    supp2.pdf supp2.pdf
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minimum score must view must submit must contribute