Convex optimization by Stephen Boyd and Lieven Vandenberghe
Lectures on Modern Convex Optimization by Aharon BenTal and Arkadi. Nemirovski
Convex Optimization Theory by Dimitri P. Bertsekas
(Real analysis) Principles of Mathematical analysis by Walter Rudin (3rd edition)
(Linear algebra)Linear Algebra and its Applications by Gilbert Strang (4th edition)
Satya Jayadev Pappu, ee15d202@ee.iitm.ac.in
Kiran Rokade, ee17s011@smail.iitm.ac.in
Ramaseshan, ee17d402@smail.iitm.ac.in
Vijayanand, ee17s046@smail.iitm.ac.in
Siva Shanmugam, ee17s024@smail.iitm.ac.in
Aathira Prasad, ee18s033@smail.iitm.ac.in
Neema Davis, ee14d212@ee.iitm.ac.in
CVX assignment- 10%
Mid-sem - 30%
End-sem - 40%
Tutorial quiz - 20%
Tutorial questions would be uploaded on Moodle. There would a tutorial session for each tutorial conducted mostly during the Friday lecture hour.
Basics of linear algebra
To recognize and formulate convex optimization problems
Mathematical preliminaries: real analysis - ordered sets, metric spaces, norm, inner product, open, closed and compact sets, continuous and differentiable functions
Convex sets: Standard examples of convex sets, operations preserving convexity, separating and supporting hyperplane, generalized inequalities
Convex functions: First and second order conditions for convexity, examples, operations preserving convexity, quasiconvex functions, logconcave functions
Convex optimization problems: Standard form, equivalent formulation, optimality criteria, quasi convex optimization, linear programming, quadratic programming, cone programming, SDPs, LMIs, geometric programming, Multi-objective optimization
Duality: Lagrangian duality, weak and strong duality, slater's condition, optimality condition, complementary slackness, KKT conditions
Some basic algorithms (if time permits)