Math 273b, Section 1, Spring 2016
Calculus of Variations
Lecture Meeting Time: Mon, Wed, and Fri 2.00pm - 2.50pm.
Lecture Location: MS 5138.
Instructor: Luminita A. Vese
Office: MS 7620 D
Office hours: TBA
E-mail: lvese@math.ucla.edu
General Course Description: Application of abstract mathematical theory to optimization problems of calculus of variations.
References:
I. Ekeland and R. Temam, Convex Analysis and Variational Problems, SIAM, 1999 (new edition)
Online
access restricted to UC campuses (2nd edition)
J. Nocedal and S.J. Wright, Numerical Optimization, Springer
Series in Operations Research, Springer 1999 (1st or 2nd edition).
Online access
restricted to UC campuses (2nd 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.
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.
Dimitri P. Bertsekas, with Angelia Nedic and Asuman E. Ozdaglar, Convex Analysis and Optimization.
L.C. Evans, Partial Differential Equations , Chapter 8.
H. Attouch, G. Buttazzo, and G. Michaille, Variational Analysis in Sobolev and BV Spaces: applications to PDE's and optimization, MPS-SIAM 2006.
Specific topics:
Abstract formulations in calculus of variations and applications to minimization problems on Sobolev spaces. Several sections from Ekeland-Temam will be presented.
Abstract minimization problems, existence of minimizers, applications, duality techniques in the continuous case (Ekeland-Temam), polar functions, Lagrangians, saddle points.
Duality applied to a particular case on finite dimensional optimization.
Several notions of differentiability; characterization of minimizers; computation of Euler-Lagrange equation; associated gradient descent method for a general problem "Min F(u)" for u in V that decreases the objective function (associated time-dependent Euler-Lagrange equation).
Applications to abstract minimization problems and to minimization problems on Sobolev spaces; computation of the dual problem.
Sobolev gradients
Shape optimization and applications to image processing.
Other topics
Links:
Matlab Optimization Toolbox
Optimization Online
Optimization Center at Northwestern University
SIAM Activity Group on Optimization
Numerical Recipies
NEOS Guide
Convex Analysis and Optimization by Dimitri P. Bertsekas
Computational Convex Analysis - CCA numerical library by Yves Lucet
Assignments Policy:
There will be several homework assignments with theoretical and computational questions.
Notes:
Summary of optimality conditions
Notes on Stable and Normal Problems (following Ekeland-Temam)
Connections with the finite dimensional case
Duality Examples
Notations for Sobolev Spaces
Homework Assignments, Projects & Practice Problems:
Homework #1 (due on Wednesday, April 20)
Latex file
Homework #2 (due on Monday, May 2nd)
Latex file
Homework #3 (due on Friday, June 3rd)
Latex file