# Lecture plan

Date Topics Reading Slides and code
Introduction, notion and existence of solutions
Week 2 Overview
Formulation of a minimization problem
Definitions of minima
Lower semi-continuity and coercivity
Existence theorems for minima
First order necessary conditions
Second order necessary and sufficient conditions
N&W, Chapters 1, 2.1

Minimisers of Optimisation Problems
Introduction

Definitions
Week 3 Convex functions
First and second order characterisations
Basic Properties of Convex Functions Optimality conditions
Line search methods for free optimisation
Gradient descent method
Exact line search methods
Armijo conditions
Wolfe conditions
N&W, Chapter 3.1
Convergence of descent methods with backtracking
Convex functions
undamped_graddesc.py
graddesc_backtracking.py
Week 4 Exact line search
Goldstein conditions
(strong and weak) Wolfe conditions
Algorithmic approach to Wolfe conditions
Convergence rate of the gradient descent method
Newton's method
Damped Newton method
Regularisation of the damped Newton method
N&W, Chapter 3.1
Convergence of descent methods with backtracking

N&W, Chapter 3.3

N&W, Chapter 3.5
Backtracking gradient descent

Line searches
Week 5 Linear Conjugate Gradient method
Fletcher–Reeves method
Polak–Ribière method
Zoutendijk's result
Summary
N&W, Chapter 5

N&W, Chapter 3.2
Gradient descent and Newton

CG methods
Theory and basic methods for constrained optimisation
Week 6 Constrained optimisation over convex sets
Feasible directions
First order optimality conditions for convex sets
Projections on convex sets
Gradient projection method
Normal cones
Conditions for linear constraints
Tangent cones for nonconvex sets
Necessary conditions for non-convex sets
Linearised feasible directions
Optimisation with convex constraints

N&W, Chapter 12.1–12.4.1)
Nonlinear CG methods

Optimisation with convex constraints
Week 7 Constraint qualifications
LICQ
Farkas' Lemma
KKT-conditions
Second order necessary and sufficient conditions
N&W, Chapter 12.2–12.5 Linear and non-convex constraints

Constraint qualifications
Week 8 Quadratic penalty method
Nonsmooth penalty methods
Augmented Lagrangian method
N&W, Chapters 17.1–17.3
Week 9 Penalty methods for inequality constraints
Logarithmic barrier methods
N&W, Chapters 17.1–17.2, 17.4
N&W, Chapter 19.1
KKT conditions and more

Barrier methods
Advanced methods for free optimisation
Quasi-Newton methods
SR1-method
DFP and BFGS method
N&W, Chapters 6.1, 6.2, 6.4
Week 10 BFGS method
Properties of Quasi-Newton methods
Limited memory BFGS
Trust region methods
Exact solution of the trust region problem
N&W, Chapters 6.1, 6.2, 6.4, 7.2

N&W 4.1, 4.2, 4.3
SR1 and DFP methods

Quasi-Newton methods
Week 11 Cauchy point
Convergence of trust region methods
Dogleg method
Non-linear least squares problems
Gauß–Newton method
Levenberg–Marquardt method
N&W 4.1, 4.2

N&W 10.1, 10.3
Trust region methods

Trust region methods, II
Advanced methods for constrained optimisation
Lagrangian duality
Primal and dual linear programmes
Weak duality
N&W, Chapter 12.9
Week 12 Strong duality for convex problems
Saddle point properties
Dual projected gradient method
Primal-dual interior point method for linear programming
Central path
Path following methods
N&W, Chapter 12.9
Lagrangian Duality

N&W, Chapters 13.1,14.1
Non-linear Least squares and Lagrangian duality

Lagrangian duality
Week 13 Quadratic programming
Direct solution of equality constrained problems
Active set methods
SQP method (equality constraints)
Newton's method for the KKT conditions
Merit functions
Maratos effect
N&W, Chapters 16.1, 16.2, 16.5, 16.7

N&W, Chapters 18.1, 18.3, 15.4, 15.5
Interior point methods

Quadratic programming
Week 14 SQP method (inequality constraints) N&W, Chapters 18.1, 18.3 SQP method
Summary
Thursday: Summary of theory
Friday: Summary of numerical methods
Questions
Summary
Week 15 Primal dual methods for convex problems
Questions
Some poor, handwritten notes
1)
12.2 is particularly relevant here.
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