Computational Optimization
  • Computational Optimization
  • Basics
  • QR Factorization
  • Regularization
  • Gradients
  • Nonlinear Least-Squares
  • Gradient Descent
  • Descent Methods
  • Scaled Descent
  • Cholesky Factorization
  • Linear Constraints
  • Convex Set
  • Convex Functions
  • Global Optimal of Convex Optimization
  • Optimality for Convex Optimization
  • Projection Onto Convex Sets
  • Stochastic Gradient Descent (SGD)
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Computational Optimization

This is the study notes for CPSC 406 at UBC. Website for the course: https://friedlander.io/ubc-cpsc-406/.

Here is the outline for the notes:

Basics

QR Factorization

Regularization

Gradients

Nonlinear Least-Squares

Gradient Descent

Descent Methods

Scaled Descent

Cholesky Factorization

Linear Constraints

Convex Set

Convex Functions

Global Optimal of Convex Optimization

Optimality for Convex Optimization

Projection Onto Convex Sets

Stochastic Gradient Descent (SGD)

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Last updated 1 year ago