Student presentations, which form the basis for class discussions, are available below. Full citations for the related readings can be found on the readings page.
| SES # | TOPICS |
|---|---|
| 1 |
_MAXCUT; Semidefinite Programming; and the Goemans-Williamson Paper, “Improved Approximation Algorithms for Maximum Cut and Satisfiability Problems Using Semidefinite Programming.” _ Presentation courtesy of Rob Freund (PDF). This is a summary presentation based on: Goemans, Michel X., and David P. Williamson. “Improved Approximation Algorithms for Maximum Cut and Satisfiability Problems Using Semidefinite Programming.” Journal of the ACM 42, no. 6 (November 1995): 1115-45. |
| 2 |
_Dunagan and Vempala Paper, “Simple Polynomial-Time Rescaling Algorithm for Solving Linear Programs.” _ Dunagan, John, and Santosh Vempala. “A Simple Polynomial-Time Rescaling Algorithm for Solving Linear Programs.” In Proceedings of the 36th Annual Association for Computing Machinery Symposium on Theory of Computing. New York, NY: ACM Press, 2004. Storn and Price Paper, “Differential Evolution - A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces.” Presentation courtesy of David Craft. Used with permission (PDF). This is a summary presentation based on: Storn, Rainer, and Kenneth Price. “Differential Evolution - A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces.” Journal of Global Optimization 11 (1997): 341-59. |
| 3 |
Clarkson Paper, “Las Vegas Algorithms for Linear and Integer Programming When the Dimension Is Small.”
Presentation courtesy of Susan Martonosi. Used with permission (PDF). This is a summary presentation based on: Clarkson, Kenneth L. “Las Vegas Algorithms for Linear and Integer Programming When the Dimension Is Small.” Journal of the ACM 42, no. 2 (March 1995): 488-99. _Motwani and Raghavan, Chapter 9 in Randomized Algorithms. |
| 4 |
_Kalai Paper #1, “A Subexponential Randomized Simplex Algorithm.”
Kalai Paper #2, “Linear Programming, the Simplex Algorithm and Simple Polytopes.”_ Both presentations courtesy of Dan Stratila. Used with permission (PDF). This is a summary presentation based on two papers: Kalai, Gil. “A Subexponential Randomized Simplex Algorithm (Extended Abstract).” In Proceedings of the 24th Annual Association for Computing Machinery Symposium on Theory of Computing. New York, NY: ACM Press, 1992. and, Kalai, Gil. “Linear Programming, the Simplex Algorithm and Simple Polytopes.” Jerusalem, Israel: Hebrew University of Jerusalem, May 1997. |
| 5 |
Solis and Wets Paper, “Minimization by Random Search Techniques.”
Presentation courtesy of Michele Aghassi. Used with permission (PDF). This is a summary presentation based on: Solis, F. J., and R. J-B. Wets. “Minimization by Random Search Techniques.” Mathematical Operations Research 6 (1981): 19-30. Romeijn Thesis Book, “Global Optimization by Random Walk Sampling Methods.” |
| 6 |
Zabinsky and Smith Paper, “Pure Adaptive Search in Global Optimization.”
Presentation courtesy of Michael Yee. Used with permission (PDF). This is a summary presentation based on: Zabinsky, Zelda B., and Robert L. Smith. “Pure Adaptive Search in Global Optimization.” Mathematical Programming 55 (1992): 323-38. Presentation courtesy of Michael Yee and Kwong-Meng Teo. Used with permission (PDF). This is a summary presentation based on: Zabinsky, Zelda B., and Robert L. Smith. “Pure Adaptive Search in Global Optimization.” Mathematical Programming 55 (1992): 323-38. |
| 7 | Simonovits Paper, “How to Compute the Volume in High Dimension?” |
| 8 | Romeijn and Smith Paper, “Simulated Annealing for Constrained Global Optimization.” |
| 9 |
_Bertsimas and Vempala Paper, “Solving Convex Programs by Random Walks.”
Zabinsky, Smith, etc. Paper, “Improving Hit-and-Run for Global Optimization.”_ |
| 10 |
_Zabinsky, Graesser, etc. Paper, “Global Optimization of Composite Laminates Using Improving Hit and Run.”
Sanjeev Paper, “Approximation Schemes for NP-hard Geometric Optimization Problems: A Survey.”_ |
