CS 733: Computational Methods for Large Sparse Systems - Fall 2012 (Also ECE and Math)

Schedule

  Lecture:     8:50 - 9:40 MWF, 1325 CS
               Start at 8:40 from Sep 7 to Dec 12
  Mailing list: compsci733-1-f12@lists.wisc.edu 
  Course URL: http://www.cs.wisc.edu/~cs733-1 

Instructor: Michael C. Ferris

  Office:       4381 CS
  Telephone:    262-4281
  E-mail:       ferris@cs.wisc.edu
                I will respond to the class mailing list, including
		your original message in most cases.
  Office Hours: 10:00 - 11:00 Mondays, 11:00 - 12:00 Wednesdays
 
  Class cancelled: September 14, 2012; September 26, 2012; November
  21, 2012
 

Recommended Textbooks:

• Note that the SIAM books are mostly available via the UW Madison license with SIAM for e-books.
• Applied Numerical Linear Algebra, J. Demmel, SIAM, 1997.
• Iterative Methods for Sparse Linear Systems, Saad, SIAM, 2003.
• Iterative Methods for Solving Linear Systems,/a>, Greenbaum, SIAM, 1997.
• Direct Methods for Sparse Linear Systems, T. Davis, SIAM, 2006.
• Learning Matlab, T. A. Driscoll, SIAM, 2009.
• Matlab Guide, D. Higham and N. Higham, SIAM 2005.
• Matrix Computations, Golub and Van Loan, Johns Hopkins University Press, Third Edition, 1996.
• Direct Methods for Sparse Matrices, Duff, Erisman and Reid, Oxford Science Publications, 1990.
• Numerical Linear Algebra, L.N. Trefethen and D. Bau, SIAM, 1997.

General Course Information

• Course Overview
  • Introduction: linear algebra, storage schemes.
  • Direct methods (factorizations)
    • Gaussian elimination: dense, error analysis, sparse.
    • Local pivotal strategies.
    • Matrix modifications.
  • Iterative linear solvers.
  • Eigenvalue problems and eigenvectors.
  • Sparse least squares.
  • Sparse nonlinear equations.
  • Optimization applications.
  • Structure exploitation.
  • Algorithms for modern computing architectures.
• Prerequisite (CS 367, 412 and Math 340) or (CS 367 and ECE 416) or (Consent of Instructor).

Scribing

• Each student in turn will need to scribe a number of classes. Resulting output should be in pdf and source to produce this must also be provided.
• Sign up list
  • Sep 5: Hyunwoo Kim
  • Sep 7: Merve Bodur
  • Sep 10: Zev Friedman
  • Sep 12: Youngdae Kim
  • Sep 17: Andrew Seidl
  • Sep 19: Lei Li
  • Sep 21: Yu Sun
  • Sep 24: Zhigeng Geng
  • Sep 28: Matthew McDaniel
  • Oct 1: Sathya Ravi
  • Oct 3: Honghao Zheng
  • Oct 5: Prathmesh Prabhu
  • Oct 8: Maxwell Collins
  • Oct 10: Corey Henderson
  • Oct 12: Alexander Borden
  • Oct 15: Yongjia Song
  • Oct 17: Niley Vaish
  • Oct 19: Taedong Kim
  • Oct 22: Nathan Mitchell
  • Oct 24: Hyunwoo Kim
  • Oct 26: Merve Bodur
  • Oct 29: Zev Friedman
  • Oct 31: Youngdae Kim
  • Nov 2: Andrew Seidl
  • Nov 5: Lei Li
  • Nov 7: Yu Sun
  • Nov 9: Zhigeng Geng
  • Nov 12: Matthew McDaniel
  • Nov 14: Sathya Ravi
  • Nov 16: Honghao Zheng
  • Nov 19: Prathmesh Prabhu
  • Nov 26: Corey Henderson
  • Nov 28: Alexander Borden
  • Nov 30: Yongjia Song
  • Dec 3: Niley Vaish
  • Dec 5: Taedong Kim
  • Dec 7: Nathan Mitchell
  • Dec 10: Maxwell Collins

Grading

• Grades for the class will be available at Learn@UW. You will need to log-on, move to the course page, and use the "Grades" tab at the top of the page.
• Approximately: 30% Homework, 20% Scribing, 50% Final
• You may discuss the assignments with your classmates. However, you may not share any code, copy solution from another person, or carry out an assignment together. Discussion should only involve verbal communication. All assignments need to be written up entirely separately.

  Submitting someone else's work as your own is academic misconduct. Such cheating and plagiarism will be dealt with in accordance with University procedures (see the Academic Misconduct Guide for Students) .

Assignments and examinations

• 1 Assignment per week approximately. Homework due at beginning of class one week after assigned unless otherwise noted.
• Examinations are closed book, with the exception that 1 handwritten sheet (standard size paper) can be brought in to the examination.
• No Midterm Examination
• Final Examination - will be no later than December 14.

Handouts:

• Mex function introduction.
• CSPARSE package.
• Sparse Matrices in Matlab (Gilbert, Moler and Schreiber).
• Sparse Matrices (Parallel Implementation) in Matlab (Shah and Gilbert).
• Implementation of Markovitz notes handout.
• Operator Splitting for Monotone Affine VI.

Programming Assignments and Homeworks

All assignments need to be written up entirely separately. You may discuss the problems informally with others in the class, but the discussions must not include code or explicit solutions to any of the problems.
• Most homeworks will be handed in either in hard copy or using the drop box facility of Learn@UW. The drop box menu is found in the top menu bar once you have logged into the system.
• Homework 1 Additional files mentioned are: Makefile and cs_ex2-1.c. Solutions posted: hwk1sol.zip
• Homework 3 (due Friday October 5, 2012). Additional files mentioned are: hwk3.m, ldu.m, spfind.c, hwk2.mat and hwk3.mat
• Homework 4 (due Monday October 15, 2012).
• Homework 5 (due Monday October 29, 2012). You can see more output using spparms('spumoni',2) in Matlab. While A\b might be the best way to solve this, you may only use this to check your answers, I am more interested in the structure of the matrix you factor, and how you exploit that factorization. ex0.mat, ex1.mat and ex2.mat
• Homework 6 (due Friday November 9, 2012). frontal.c and hwk6.m
• Homework 7 (due Monday November 19, 2012).
• Homework 8 (due Friday December 14, 2012). solve11a.m, solve11b.m, solve11c.m, hwk8.mat and hwk8.m

Miscellaneous

• Archive of Class Mailing List. You will need to enter your cs account login and password to access this archive.
• Computer Systems Laboratory Main Page
• Instructional Facilities
• Account Activation
• Instructional Account Policies
• Forwarding Email
• Optimization at the University of Wisconsin
• Courses related to 733
• Nonlinear Programming FAQ
• NEOS Optimization Guide