LINEAR PROGRAMMING ONLINE COURSE

Free Online Course (Audit) Certificate Available 5-7 hours a week 20th Oct, 2014 second Sep, 2013
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Linear Programming (LP) is arguably among the many vital optimization problems in used mathematics and also design. The Simplex algorithm to fix straight programs is widely regarded as one among the "top ten"algorithms of the 20th century. Linear Programs arise in almost all fields of design consisting of operations research study, statistics, machine learning, manage mechanism design, scheduling, formal verificationand computer system vision. It develops the basis for numerous viewpoints to fixing difficult combinatorial optimization difficulties via randomization and approximation.

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The major goals of this coursewill certainly be to:

1. Understand the standard concept behind LP, algorithms to fix LPs, and the basics of (mixed) integer programs (ILP).

2. Understand also crucial and emerging applications of LP and also ILPs to economic difficulties (optimal resource alplace, scheduling problems), machine learning (SVM), and also combinatorial optimization problems.

At the end of the course, the successful student will certainly be able to cast miscellaneous troubles that may aincrease in her research as optimization difficulties, understand also the instances wright here the optimization problem will certainly be direct, choose appropriate solution techniques and interpret results as necessary. This is mostly considered a helpful capability in many kind of study areas.

See more: Course Kit Online Training Curriculum, Karlsruhe Institute Of Technology Online Courses


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Syllabus


Introductory Material

Review to Liclose to Programming.Week #1:The Diet Problem.Linear Programming Formulations.Tutorials on utilizing GLPK (AMPL), Matlab, CVX and also Microsft Excel.The Simplex Algorithm (basics).Week #2:Handling boundless problemsDegeneracyGeomeattempt of SimplexInitializing Simplex.Cycling and also the Use of Bland"s dominion.Week #3:Duality: dual variables and dual linear regimen.Strong duality theorem.Complementary Slackness.KKT conditions for Linear Programs.Understanding the dual problem: shadow expenses.Extra: The revised simplex technique.Week #4:Advanced LP formulations: norm optimization.Least squares, and also quadratic programming.Applications #1: Signal rebuilding and construction and De-noising.Applications #2: Regression.Week #5:Integer Liclose to Programming.Integer vs. Real-valued variables.NP-completeness: basic introduction.Reductions from Combinatorial Problems (SAT, TSP and Vertex Cover).Approximation Algorithms: Overview.Week #6:Branch and also Bound MethodCutting Plane MethodWeek #7:Applications: solving puzzles (Sudoku), reasoning about units and various other applications.Group and also Machine Learning
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Taught by

Sriram Sankaranarayanan


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