Spring 2016

**Office hours:** Tuesdays 2-4pm, Wednesdays 12 noon-2pm, or by appointment.

**Material
on reserve** in the library.

**Scores** are available on LMS

- Homework 1: Mean 51.5/60, median 54.5, min 36, max 60, standard deviation 8.0.

**Projects:**

** Midterm Exam: **

** Homework:**

- Homework 1. Here are the solutions.

**Information about
AMPL**.

** Notes:**
Handwritten scanned copies of my notes from previous semesters (pdf files):

- Introduction, basic feasible solutions,
duality, and the simplex algorithm.
- Introduction: Lecture 1: Pages 1-8. Pages 5-8 are covered in the first three handouts
- Basic feasible solutions, degeneracy: Lecture 2: Pages 9-19. Here are typewritten notes for part of this material.
- Duality: Lecture 3: Pages 20-29. Here are typewritten notes for part of this material.
- The simplex algorithm: Lecture 4: Pages 30-42. See also two handouts.
- Multiple optimal solutions, handling free variables and upper bounds: Lecture 5: Pages 43-50. See also a handout.

- Revised simplex, resolution.
- The Klee Minty cube: Lecture 7.
- Probabilistic analysis of the simplex method: Lecture 7. Focus on the results on pages 4, 5, and 9.
- The ellipsoid method: Lecture 8.
- Dantzig-Wolfe decomposition,
cutting stock problems, network simplex, multicommodity network flows.
- Lecture 8: introduction to Dantzig-Wolfe, pages 1-5.
- Lectures 9 and 10: Dantzig-Wolfe iteration and behavior, block angular structure, pages 6-17. Also, an example of Dantzig-Wolfe decomposition.
- Lectures 10 and 11: Lagrangian relaxation, cutting stock problems, pages 18-27.
- Lectures 12 and 13: Network simplex, multicommodity network flow problems, pages 28-37. Here are some notes on network flows.

- Stochastic programming.
- Lecture 14: pages 1-5.
- Lecture 15: pages 6-11, 17-19.
- Lecture 16: pages 20-26.
- The results on sample average approximation can be found in a tutorial on stochastic programming by Shapiro and Philpott.

- Introduction
to interior point methods:
strict complementarity, the central path,
primal-dual framework, complexity theory, affine methods,
the centering direction, a potential-reduction method.

Pages 24-27, 29, 1, 3-7 covered in Lecture 17.

Pages 7-8, 2, 9-17 covered in Lecture 18.

Pages 18-22 covered in Lecture 19. - Barrier methods.

Pages 6-18 covered in Lecture 19. - Long-steps, quadratic convergence, predictor-corrector,
finite termination, crossover, infeasible methods.

Pages 11-16 covered in Lecture 20.

Pages 6-10 covered in Lecture 22. - Homogenized methods,
Mehrotra's predictor-corrector, sparse linear algebra,
interior point cutting plane methods.

Pages 1-4 covered in Lecture 20.

Pages 5-9 covered in Lecture 22.

Pages 10-14 summarized in ten minutes in Lecture 22.

Pages 19-25 covered in Lecture 23.

Slides 3/72 to 30/72 of this talk on interior point cutting plane methods were covered in Lecture 23. The talk is based on a paper. - SDP
and SOCP.

Pages 1-7 covered in Lecture 23.

Pages 18-20, 14-15, and 17 covered in Lecture 24.

Pages 13, 27, 30, and 22-24 covered in Lecture 25.

Pages 8-12, 21, 25-26, and 28 covered in Lecture 26.

Pages 31-33 covered in Lecture 27. The example on sensor location is here.~~Page 16 covered in Lecture 28.~~

~~Lecture 28: Pages 35/72 to 53/72 and 67/72 to 69/72 from this talk on interior point cutting plane methods should be covered. It is based on a paper.~~

The book Convex Optimization, by Boyd and Vandenberghe, contains a wealth of material on SDP, SOCP, and conic programming. - Completely positive programming. Lecture 28.
~~The material on linear programs with complementarity constraints in Lecture 28 is from two talks: slides 4/54 to 14/54 from this one and slides 12/41 to 24/41 from this one.~~

- Linear algebra. (Lecture 1.)
- Subspaces, affine sets, convex sets, and cones. (Lecture 1.)
- Dimension, polyhedra, and faces. (Lecture 1.)
- An iteration of the simplex algorithm and the algorithm. (Lecture 4.)
- Handling upper bounds in the simplex algorithm. (Lecture 5.)
- The dual simplex algorithm. (Lecture 6.)
- Extreme points and extreme rays of polyhedra. (Lecture 8.)
- An example of Dantzig-Wolfe decomposition. (Lecture 9.)

**Papers and resources:**

- An electronic copy of the textbook for the second half of the course is available for free through the library: S. Wright, Primal-dual Interior Point Methods, SIAM, 1997. Note: more generally, SIAM e-books are free to RPI students through the library. In addition, SIAM membership is free for RPI students, and SIAM books are discounted 30% for members if you buy directly from SIAM.
- The book A First Course in Linear Optimization, Second Edition, Reex Press, 2013/4/5 by Jon Lee is available online.
- The book Convex Optimization, by Boyd and Vandenberghe, contains a wealth of material on SDP, SOCP, and conic programming.
*Introduction to Stochastic Programming*by Birge and Louveaux is available online via the RPI library.- A computational study of Dantzig-Wolfe decomposition, the doctoral thesis of James Tebboth.
- Myths and counterexamples in linear programming (and other areas of optimization). This site shows that you have to be careful about your assumptions when you state some things that are "obvious" in linear programming.
- Issue 87 of the Mathematical Optimization Society newsletter Optima, discussing the geometry of polyhedra and simplex pivoting rules.

John Mitchell's homepage. Mathematics Course Materials.