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This course is a study of Behavior of Algorithms and covers an area of current interest in theoretical computer science.
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English
English [CC]
FREE
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Description
The topics vary from term to term. During this term, we discuss rigorous approaches to explaining the typical performance of algorithms with a focus on the following approaches: smoothed analysis, condition numbers/parametric analysis, and subclassing inputs.
Course content
- The Condition Number Unlimited
- The Largest Singular Value of a Matrix Unlimited
- Gaussian Elimination Without Pivoting Unlimited
- Smoothed Analysis of Gaussian Elimination Without Pivoting Unlimited
- Growth Factors of Partial and Complete Pivoting Unlimited
- Spectral Partitioning Introduced Unlimited
- Spectral Partitioning of Planar Graphs Unlimited
- Spectral Parititioning of Well-Shaped Meshes and Nearest Neighbor Graphs Unlimited
- Smoothed Analysis and Monotone Adversaries for Bandwidth and Graph Bisection Unlimited
- Introduction to Linear Programming Unlimited
- Strong Duality Theorem of Linear Programming Unlimited
- Analysis of von Neumann’s Algorithm Unlimited
- Worst-Case Complexity of the Simplex Method Unlimited
- The Expected Number of Unlimited
- The Expected Number of Facets of the Convex Hull of Gaussian Random Points in the Plane (cont.) Unlimited
- The Expected Number of Facets of the Shadow of a Polytope Given by Gaussian Random Constraints Unlimited
- The Expected Number of Facets of the Shadow of a Polytope Given by Gaussian Random Constraints: Angle Bound and Overview of Phase 1 Unlimited
Instructor
Massachusetts Institute of Technology
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