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Convex optimization and engineering applications

01OUWQW, 01OUWNG, 01OUWOQ, 01OUWOV

A.A. 2019/20

Course Language

Inglese

Course degree

Master of science-level of the Bologna process in Mechatronic Engineering (Ingegneria Meccatronica) - Torino
Master of science-level of the Bologna process in Ingegneria Matematica - Torino
Master of science-level of the Bologna process in Ingegneria Elettronica (Electronic Engineering) - Torino
Master of science-level of the Bologna process in Ingegneria Informatica (Computer Engineering) - Torino

Course structure
Teaching Hours
Lezioni 40
Esercitazioni in aula 20
Tutoraggio 40
Teachers
Teacher Status SSD h.Les h.Ex h.Lab h.Tut Years teaching
Calafiore Giuseppe Carlo Professore Ordinario ING-INF/04 40 20 0 0 10
Teaching assistant
Espandi

Context
SSD CFU Activities Area context
ING-INF/04 6 D - A scelta dello studente A scelta dello studente
2019/20
Optimization is a technology that can be used for devising effective decisions or predictions in a variety of contexts, ranging from production planning, to engineering design, finance, machine learning and data science, to mention just a few. In simplified terms, the process for reaching a decision starts with a phase of construction of a suitable mathematical model for a concrete problem, followed by a phase where the model is solved by means of suitable numerical algorithms. An optimization model typically requires the specification of a quantitative objective criterion of goodness for our decision, which we wish to maximize (or, alternatively, a criterion of cost, which we wish to minimize), as well as the specification of constraints, representing the physical limits of our decision actions, budgets on resources, design requirements than need be met, etc. An optimal design is one who gives the best possible objective value, while satisfying all problem constraints. While a generic optimization approach typically leads to computational problems that are very hard to solve in practice, convex problems, which are the core of this course, possess special properties that make them amenable to very efficient numerical solution techniques. This course therefore concentrates on recognizing, modeling and solving convex optimization problems that arise in several scientific contexts (engineering, finance, control, data science, etc.), and in showing the relevance of these methodologies in practical applications.
Optimization is a technology that can be used for devising effective decisions or predictions in a variety of contexts, ranging from production planning, to engineering design, finance, machine learning and data science, to mention just a few. In simplified terms, the process for reaching a decision starts with a phase of construction of a suitable mathematical model for a concrete problem, followed by a phase where the model is solved by means of suitable numerical algorithms. An optimization model typically requires the specification of a quantitative objective criterion of goodness for our decision, which we wish to maximize (or, alternatively, a criterion of cost, which we wish to minimize), as well as the specification of constraints, representing the physical limits of our decision actions, budgets on resources, design requirements than need be met, etc. An optimal design is one who gives the best possible objective value, while satisfying all problem constraints. While a generic optimization approach typically leads to computational problems that are very hard to solve in practice, convex problems, which are the core of this course, possess special properties that make them amenable to very efficient numerical solution techniques. This course therefore concentrates on recognizing, modeling and solving convex optimization problems that arise in several scientific contexts (engineering, finance, control, data science, etc.), and in showing the relevance of these methodologies in practical applications.
To give students the tools and training to recognize convex optimization problems that arise in engineering and other branches of science; 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.
To give students the tools and training to recognize convex optimization problems that arise in engineering and other branches of science; 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.
Good knowledge of linear algebra, geometry, calculus, and some exposure to probability. Exposure to numerical computing, optimization, systems and control theory, and application fields is helpful but not strictly required.
Good knowledge of linear algebra, geometry, calculus, and some exposure to probability. Exposure to numerical computing, optimization, systems and control theory, and application fields is helpful but not strictly required.
Introduction, convex sets and convex functions. Optimization problems in standard form, optimality criteria. Systems of linear equations, Least Squares (LS), Linear Programming (LP), Ell-one 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 (e.g. FIR filter design, antenna array design, sidelobe level minimization, etc.). Linear Matrix Inequalities (LMI) and semidefinite programming (SDP). Geometric programming (GP). Software tools (CVX and/or YALMIP). Applications: data-fitting, approximation and estimation, uncertain and robust Least Squares, portfolio optimization. Geometrical problems: containment of poyhedra, classification, Lowner-John ellipsoids, linear discrimination, support vector machines. Focus topics (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.
Introduction, convex sets and convex functions. Optimization problems in standard form, optimality criteria. Systems of linear equations, Least Squares (LS), Linear Programming (LP), Ell-one 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 (e.g. FIR filter design, antenna array design, sidelobe level minimization, etc.). Linear Matrix Inequalities (LMI) and semidefinite programming (SDP). Geometric programming (GP). Software tools (CVX and/or YALMIP). Applications: data-fitting, approximation and estimation, uncertain and robust Least Squares, portfolio optimization. Geometrical problems: containment of poyhedra, classification, Lowner-John ellipsoids, linear discrimination, support vector machines. Focus topics (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.
The course is organized in a series of lectures (about 1/3 of the course) and computer lab exercises and practice sessions (about 2/3 of the course).
The course is organized in a series of lectures (about 1/3 of the course) and computer lab exercises and practice sessions (about 2/3 of the course).
G.C. Calafiore and L. El Ghaoui, Optimization Models, Cambridge Univ. Press, 2014. S. Boyd and L. Vandenberghe; Convex Optimization, Cambridge Univ. Press, 2004.
G.C. Calafiore and L. El Ghaoui, Optimization Models, Cambridge Univ. Press, 2014. S. Boyd and L. Vandenberghe; Convex Optimization, Cambridge Univ. Press, 2004.
Modalità di esame: Prova scritta (in aula);
Exam: Written test;
Gli studenti e le studentesse con disabilità o con Disturbi Specifici di Apprendimento (DSA), oltre alla segnalazione tramite procedura informatizzata, sono invitati a comunicare anche direttamente al/la docente titolare dell'insegnamento, con un preavviso non inferiore ad una settimana dall'avvio della sessione d'esame, gli strumenti compensativi concordati con l'Unità Special Needs, al fine di permettere al/la docente la declinazione più idonea in riferimento alla specifica tipologia di esame.
Exam: Written test;
The final exam consists in a written test, which will contain a mixture of methodological questions and numerical exercises (to be executed with pen and paper; use of a calculator is allowed) and which will take the form of multiple-choice questionar. The final score will be computed as a weighted average of the written exam score (70%) and a score counting the presence and participation to the lab sessions (30%).
In addition to the message sent by the online system, students with disabilities or Specific Learning Disorders (SLD) are invited to directly inform the professor in charge of the course about the special arrangements for the exam that have been agreed with the Special Needs Unit. The professor has to be informed at least one week before the beginning of the examination session in order to provide students with the most suitable arrangements for each specific type of exam.
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