This course is a study of Behavior of Algorithms and covers an area of current interest in theoretical computer science. 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. In addition to that, condition numbers/parametric analysis, and subclassing inputs will be discussed. See More: https://bit.ly/2YCSkdn

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HOME / COURSE / MATHEMATICS / BASIC PRINCIPLES OF BEHAVIOR OF ALGORITHMS
Basic Principles of Behavior of
Algorithms
( 9 REVIEWS ) 1125 STUDENTS
This course is a study of Behavior of Algorithms and covers an area of current interest in theoretical
computer science. …
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LEVEL 2 - CERTIFICATE
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HOME CURRICULUM REVIEWS
This course is a study of Behavior of Algorithms and covers an area of current interest in theoretical
computer science. 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. In addition to that, condition numbers/parametric analysis, and
subclassing inputs will be discussed.
Assessment
This course does not involve any written exams. Students need to answer 5 assignment questions to
complete the course, the answers will be in the form of written work in pdf or word. Students can
write the answers in their own time. Each answer needs to be 200 words (1 Page). Once the answers
are submitted, the tutor will check and assess the work.
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Course Credit: MIT
The Condition Number 00:05:00
The Largest Singular Value of a Matrix 00:05:00
Gaussian Elimination Without Pivoting 00:10:00
Smoothed Analysis of Gaussian Elimination Without Pivoting 00:05:00
Growth Factors of Partial and Complete Pivoting 00:10:00
Spectral Partitioning Introduced 00:05:00
Spectral Partitioning of Planar Graphs 00:05:00
Spectral Parititioning of Well-Shaped Meshes and Nearest Neighbor
Graphs
00:05:00
Smoothed Analysis and Monotone Adversaries for Bandwidth and Graph
Bisection
00:05:00
Introduction to Linear Programming 00:05:00
Strong Duality Theorem of Linear Programming 00:10:00
Analysis of von Neumann’s Algorithm 00:10:00
Worst-Case Complexity of the Simplex Method 00:05:00
The Expected Number of Facets of the Convex Hull of Gaussian Random
Points in the Plane – Part I
00:05:00
COURSE CURRICULUM

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The Expected Number of Facets of the Convex Hull of Gaussian Random
Points in the Plane – Part II
00:05:00
The Expected Number of Facets of the Shadow of a Polytope Given by
Gaussian Random Constraints – Part I
00:05:00
The Expected Number of Facets of the Shadow of a Polytope Given by
Gaussian Random Constraints – Part II
00:10:00
Assessment
Submit Your Assignment 00:00:00
Certi cation 00:00:00
COURSE REVIEWS
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