Optimization

Review focus¶

• Objective functions, variables, constraints, gradients, Hessians, and convexity

• Local and global optima, high dimensionality, and non-convex landscapes

• Line search, gradient descent, conjugate gradient, Newton’s method, and BFGS

• Simulated annealing, basin hopping, and genetic algorithms

• Convergence, robustness, and choosing an optimization algorithm

• Transition-state ideas and the role of NEB

Where to review in this book¶

• Optimization Overview

• Introduction

• Local Optimization

• Global Optimization

• Choose Optimization Method

• Transition State

• Optimization Notebook