MATH 110A: OPTIMIZATION I

Note 1: Introduction to Optimization / Conditions for Local Minimizers

Note 2: Conditions for Local Minimizers (continued)

Note 3: Golden Section Search

Note 4: Golden Section Search (continued) / Fibonacci Search

Note 5: Newton's Method

Note 6: Newton's Method (continued) / Secant Method

Note 7: Line Search in Multidimensional Optimization

Note 8: Introduction to Gradient Methods / Steepest Descent Method

Note 9: Steepest Descent Method for Quadratic Problems / Newton's Method

Note 10: Analysis of Newton's Method

Note 11: Introduction to Conjugate Direction Methods

Note 12: Gram-Schmidt Process / The Conjugate Direction Algorithm

Note 13: The Conjugate Direction Algorithm (continued)

Note 14: Introduction to Quasi-Newton Methods / Approximating the Inverse Hessian

Note 15: The Rank One Correction Formula

Note 16: Least-Squares Analysis / The Recursive Least-Squares Algorithm

Note 17: Solution with Minimum Norm / Kaczmarz's Algorithm

Note 18: Solving Linear Equations in General

Quiz 1: Conditions for Local Minimizers

Quiz 2: Golden Section Search / Fibonacci Search

Quiz 3: Newton's Method / Secant Method

Quiz 4: Steepest Descent Method

Quiz 5: Conjugate Direction Methods

Quiz 6: The Conjugate Direction Algorithm / The Rank One Correction Formula

Quiz 7: Least-Squares Analysis / Solution with Minimum Norm

Homework 2 (Solution): Golden Section Search / Fibonacci Search

Homework 3 (Solution): Newton's Method / Secant Method

Homework 4 (Solution): Steepest Descent Method

Homework 5 (Solution): Conjugate Direction Methods

Homework 6 (Solution): Quasi-Newton Methods