## Math 273, Section 1, Fall 2006

## Optimization, Calculus of Variations, and Control Theory

**Lecture Meeting Time:** MWF 12.00PM - 12:50PM.

** Lecture Location:** MS 6627.

**Instructor:** Luminita A. Vese

**Office:** MS 7620 D

**Office hours:** MWF 1-2pm, (tentative) or by appointment.

**E-mail:** lvese@math.ucla.edu

**Course Description:** Application of abstract mathematical theory to optimization problems of calculus of variations and control theory. Abstract nonlinear programming and applications to control systems described by ordinary differential equations, partial differential equations, and functional differential equations. Dynamic programming.

**References:**
I. Ekeland and R. Temam, * Convex Analysis and Variational Problems*, SIAM, 1999 (new
edition).
E. Zeidler, * Nonlinear Functional Analysis and its Applications*, Vol. III, * Variational Methods and Optimization *, Springer-Verlag 1984.
P.E. Gill, W. Murray, and M.H. Wright, * Practical Optimization*, Academic Press 1981.
J. Nocedal and S.J. Wright, * Numerical Optimization*, Springer Series in Operations REsearch, Springer 1999.
R.T. Rockafellar, * Convex Analysis*, Princeton University Press 1970.
J.-B. Hiriart-Urruty, C. Lemarechal, * Fundamentals of Convex Analysis*, Springer 2001.
S. Boyd and L. Vandenberghe,
*Convex Optimization*,
Cambridge University Press, 2004 (especially Chapters 9, 10 and 11).
M. Giaquinta, S. Hildebrandt, * Calculus of variations*, Springer, 1996 (two volumes).
D. Luenberger, * Optimization by Vector Space Methods *, John Wiley & Sons, 1969.

**Syllabus:**
Formulation of a general finite dimensional optimization problem with constraints; objective function, equality or inequality constraints, feasible region, example.
Gateaux-differentiability, computation of Euler-Lagrange equation for
F(u)=int_{x0}^{x1} L(x,u,u')dx in one dimension.
Finite dimensional unconstrained optimization: recall of Taylor's theorem; 1st and 2nd order necessary conditions; sufficient conditions for local minimizer; case of convex functions and global minimizers (pages 13-17 from Nocedal and Wright).
Examples of non-smooth optimization problems and how to transform them into smooth problems (from P.E. Gill, W. Murray, and M.H. Wright, Practical Optimization, pages 96-98).
Descent methods: definition of descent directions and steepest descent directions, step length alpha, computation of steepest descent direction for various norms (Euclidean, general quadratic norm, and l1-norm), exact line search and backtracking line search (from Chapter 9 Convex Optimization and part from Nocedal-Wright).
TBA

** Links:**
*Matlab Optimization Toolbox* (check with our computer office to see which machines, if any, have the Matlab optimization toolbox installed).
* Optimization Online *
* Optimization Technology Center (DOE and Northwestern)*
* SIAM Activity Group on Optimization *
* Numerical Recipies *
* NEOS Guide *

**Assignments Policy:**
There will be several homework assignments with theoretical and computational questions.

**Examinations:**
There will be one take-home final exam.

**Grading Policy:**
Hw and Projects 50%, Final 50%

**Homework Assignments, Projects & Practice Problems:**
*
HW #1: due on Monday, October 9*
*
HW #2: due on Wednesday, October 25 *
*
HW #3: due on Monday, November 13 *
*
HW #4: due on Monday, November 27 *
*
HW #5: due on Friday, December 8 *
(no late homework accepted)
*
Take-home final exam: due on Friday December 15, 2006 *

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Summary of optimality conditions *
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Connections with the finite dimensional case *