Course Archives Theoretical Statistics and Mathematics Unit
Course:
Optimization
Instructor:
Mathew Joseph
Room:
G23
Level:
Undergraduate
Time:
Currently offered
Syllabus
Past Exams
Syllabus:
Perron-Frobenius theory.
LINEAR PROGRAMMING: Basic notions; fundamental theorem of LP; the simplex algorithm; duality and applications. Karmarkars algorithm.
CONSTRAINED OPTIMIZATION PROBLEMS: Equality constraints, Lagrange multipliers; Inequality constraints, Karush-Kuhn-Tucker theorem; Illustrations (including situations where the above can fail).
FURTHER TOPICS: Convexity and optimization. Unconstrained optimization problems and descent methods
Reference Texts:
(a) H. Karloff: Linear Programming
(b) Sher-Cherng Fang and Sarat Puthenpura: Linear optimization and extensions Theory and algorithms
(c) R. K. Sundaram: A first course in optimization Theory
(d) S. Boyd and L. Vendenberhe: Convex Optimization
(e) S. J. Miller: Mathematics of optimization: how to do things faster
(f) D. Bertsimas and J. Tsitsikilis: Introduction to Linear Optimization
(g) D. Bertsekas: Convex Optimization Theory
Evaluation:
Mid-term
30 marks
Assignment
20 marks
Final Exam
50 marks
Total
100 marks
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Past Exams
Midterm
24.pdf
Semestral
Supplementary and Back Paper
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