** Summary:
The purpose is for you to become an expert on one of the
papers in the list posted on October 12. You will experiment with the
algorithm discussed in the paper, or illustrate the theory
described there, and identify open questions that could be
the topic of further research. Optionally, you will present
the paper to the class, instead of taking the final exam.
**

For your project, choose a paper from the list below and study it, reading background papers as necessary.

You may choose to do Part 1 only, for 100 points. In this case you will also take a final exam.

You may choose to do both Part 1 and Part 2, for 200 points. In this case you need not take the final exam.

Doing only Part 2 is not an option.

** Deadlines and points: **

** What to submit for Part 1:**
Your project must have these components:

** How to submit:**
Submit Part 1 by e-mail.

** Some questions that will be asked while evaluating Part 1:
**

** Warning: **
Lateness or plagiarism puts you in danger of failing the course.
If you use someone's ideas, cite the source.
If you use a direct quote, use quotation marks and cite the
source. And don't expect a good grade on a project that is
mostly someone else's work.

** A note on software: **

There are several Matlab packages available on the web for
various types of constrained optimization problems.
If Mathworks does not supply software for your problem,
I advise you to find a package from the web
and modify it (if necessary) for your purposes
rather than writing one from scratch.

** Part 2 **
If you choose a 200 point project, then you will also give a
half-hour talk (20 minutes + 10 minutes for questions)
during class,
explaining the contents of your paper.
Email me your slides (pdf) before your presentation.
I will assign time slots in order, Dec 9, Dec 7, Dec 2, Nov 30,
Nov 23, unless you have a specific request for an early date.
Your grade will be based on the clarity and quality of
your slides and your oral presentation, including how well your
fellow students understand what you say.

** Choose a paper from this list: **

Explicit Sensor Network Localization using Semidefinite Representations and Facial Reductions,

A quadratically convergent predictor-corrector method for solving linear programs from infeasible starting points,

Mathematical Programming Volume 67, Numbers 1-3, 383-406, DOI: 10.1007/BF01582228 pdf

A Scalable Optimization Approach for Fitting Canonical Tensor Decompositions,

Journal of Chemometrics, in press. pdf

On the implementation and usage of SDPT3 a Matlab software package for semidefinite-quadratic-linear programming, version 4.0,

Templates for Convex Cone Problems with Applications to Sparse Signal Recovery,

Alternating Direction Augmented Lagrangian Methods for semidefinite programming,

A Taxonomy of Global Optimization Methods Based on Response Surfaces,

Journal of Global Optimization,

Volume 21, Number 4, 345-383, DOI: 10.1023/A:1012771025575 pdf

A Study of Global Optimization Using Particle Swarms,

Journal of Global Optimization Volume 31, Number 1, 93-108, DOI: 10.1007/s10898-003-6454-x pdf

Recent developments and trends in global optimization,

Journal of Computational and Applied Mathematics Volume 124, Issues 1-2, 1 December 2000, Pages 209-228 doi:10.1016/S0377-0427(00)00425-8 pdf

Simulated Annealing Algorithms for Continuous Global Optimization: Convergence Conditions,

Journal of Optimization Theory and Applications Volume 104, Number 1, 121-133, DOI: 10.1023/A:1004680806815 pdf

Convergence Properties of the Regularized Newton Method for the Unconstrained Nonconvex Optimization,

Applied Mathematics & Optimization Volume 62, Number 1, 27-46, DOI: 10.1007/s00245-009-9094-9 pdf

Further Results on the Robust Regulation of a One-Dimensional Dam-River System,

Journal of Optimization Theory and Applications DOI: 10.1007/s10957-010-9729-7 pdf

Collision Avoidance for an Aircraft in Abort Landing: Trajectory Optimization and Guidance,

Journal of Optimization Theory and Applications Volume 146, Number 2, 233-254, DOI: 10.1007/s10957-010-9669-2 pdf

Large-Scale Semidefinite Programs in Electronic Structure Calculation,

Math. Programming 109 (2007), pp. 553-580. pdf

Conditioning of Semidefinite Programs,

Math Programming 85 (1999), pp. 525-540 pdf

A Comparison of Sample-based Stochastic Optimal Control Methods,

A Preconditioning Technique for Schur Complement Systems Arising in Stochastic Optimization,

Convex Approximations in Stochastic Programming by Semidefinite Programming,

Approximation accuracy, gradient methods, and error bound for structured convex optimization,

Math. Program., Ser. B DOI 10.1007/s10107-010-0394-2 pdf

Exploiting sparsity in linear and nonlinear matrix inequalities via positive semidefinite matrix completion,

Math. Program., Ser. A DOI 10.1007/s10107-010-0402-6 pdf

A Polynomial-Time Interior-Point Method for Conic Optimization, with Inexact Barrier Evaluations,

SIAM Journal on Optimization, 20:1 (2009) 548-571. DOI: 10.1137/080722825 pdf

Implementing an Interior Point Method for Linear Programs on a CPU-GPU System,

Electronic Transactions on Numerical Analysis, 28 (2008) 174-189. pdf

Blind Deconvolution Using a Regularized Structured Total Least Norm Approach,

SIAM J. on Matrix Analysis and Applications, 24 (2003) 1018-1037. pdf

Variable Projection for Nonlinear Least Squares Problems

pdf. Code: varpro.m Sample programs: varpro_example.m and adaex.m

Modified Cholesky Algorithms: A Catalog with New Approaches,

Mathematical Programming A, (2007) DOI:10.1007/s10107-007-0177-6 pdf

A Constraint-Reduced Variant of Mehrotra's Predictor-Corrector Algorithm,

Adaptive Constraint Reduction for Training Support Vector Machines,

Electronic Transactions on Numerical Analysis, 31 (2008) 156-177. pdf

Notes