


Politecnico di Torino  
Academic Year 2015/16  
01OUWOQ, 01OUWNG, 01OUWOT, 01OUWOV, 01OUWPE, 01OUWQW Convex optimization and engineering applications 

Master of sciencelevel of the Bologna process in Electronic Engineering  Torino Master of sciencelevel of the Bologna process in Mathematical Engineering  Torino Master of sciencelevel of the Bologna process in Telecommunications Engineering  Torino Espandi... 





Esclusioni: 01OVA; 01OVC; 01OVD; 01OVF; 01OVE; 01OUX; 01OUY; 01OUZ; 01OVB 
Subject fundamentals
The course concentrates on recognizing and solving convex optimization problems that arise in engineering. Convex sets, functions, and optimization problems. Basics of convex analysis. Leastsquares, linear and quadratic programs, semidefinite programming, minimax, extremal volume, and other problems. Optimality conditions, duality theory, theorems of alternative, and applications. Interiorpoint methods. Applications to signal processing, control, digital and analog circuit design, computational geometry, statistics, and mechanical engineering.

Expected learning outcomes
To give students the tools and training to recognize convex optimization problems that arise in engineering; to present the basic theory of such problems, concentrating on results that are useful in computation; to give students an understanding of how such problems are solved, and some experience in solving them.

Prerequisites / Assumed knowledge
Good knowledge of linear algebra, geometry, analysis and exposure to probability. Exposure to numerical computing, optimization, systems and control theory, and application fields is helpful but not required.

Contents
Introduction, convex sets and convex functions. Optimization problems in standard form, optimality criteria.
Systems of linear equations, Least Squares (LS), Linear Programming (LP), Ellone norm optimization, Chebychev approximation. Application examples: generation of force/torque via thrusters, uniform illumination of patch surfaces, etc. Quadratic Programming (QP) and Second Order Cone Programming (SOCP). Application examples: FIR filter design, antenna array design, sidelobe level minimization in beamforming. Linear Matrix Inequalities (LMI) and semidefinite programming (SDP). Geometric programming (GP). Introduction to software tools CVX and/or YALMIP. Applications: datafitting, approximation and estimation, trussstructural design, transistor sizing, uncertain and robust Least Squares, BoundedReal Lemma, passivity and applications in circuit theory. Geometrical problems: containment of poyhedra, classification, LownerJohn ellipsoids, linear discrimination, support vector machines. Interiorpoint methods. Focus seminars (mutually exclusive, to be offered alternatively over years):  LMIs in systems and control theory.  Sparse optimization and compressed sensing.  Convex optimization in Finance.  Convex optimization in algebraic geometry, global polynominal positivity (positivstellensaatz).  Network optimization, distributed optimization. 
Delivery modes
Lab: The course requires about 30 hours teaching and 30 hours lab activity.

Texts, readings, handouts and other learning resources
1. S. Boyd and L. Vandenberghe; Convex Optimization, Cambridge Univ. Press, 2004.
2. L. El Ghaoui, G. Calafiore; Optimization Models, Cambridge Univ. Press (in preparation) (draft to be made available to students) 
Assessment and grading criteria
Written Examination)

