|
Topic
|
Textbook
|
Weeks |
|
Introduction: Development of the optimization
methods, types of the problems, setting of Nonlinear Constraint Problem
|
ch 1. |
0.5 |
|
One-dimensional optimization: Necessary conditions,
Fibonacci and Golden Mean search, Gradient method, Newton Method,
Modifications, Search for global minimum.
|
3.2.1, 4.1 |
1.5 |
|
Review of Linear Algebra and Multivariable Calculus:
Vectors,
Matrices, Projections, Quadratic forms, Gradient, Hessian, Convexity
|
2.2.1-2.2.4,
2.2.6, 2.6 |
2 |
|
Optimality conditions for Mutivariable problem: Unconstrained
problem, Linear constraints, Nonlinear constraints
|
3.1-3.4 |
1 |
|
Unconstrained Methods: Nonsmooth functions,
Gradient method, Newton type methods, Non derivative Methods
|
4.2-4.6 |
2 |
|
Linear constraints: Search directions, Active
set methods, Linear Programming, Quadratic programming
|
5.1 - 5.3, 5.6 |
3 |
| Nonlinear constraints: Penally and barrier
methods, Gradient projection methods, Augmented Lagrangian methods, Projected
Lagrangian Method |
6.1 - 6.5 |
2 |
|
Review: Stochastic optimization, Genetic
algorithms, Large-scale problems
|
Notes |
1 |
|
Projects presentation:
|
|
1
|