IKT718 Trial lecture

Emnet er tilknyttet følgende studieprogram

• Informasjons- og kommunikasjonsteknologi, ph.d.-program
• Mobile kommunikasjonssystemer: nettverk, sikkerhet og formelle metoder, ph-d.-program
• Ph.d.-program i teknologi og realfag

Forkunnskapskrav

Probability Theory and Stochastic Processes, Linear Algebra, Mathematical analysis and Matlab programming.

Anbefalte forkunnskaper

Recommended previous knowledge IKT 438 (or similar course elsewhere).

Innhold

The course covers the following main topics (non necessarily in this order): Sub-gradient methods, cutting-plane methods, Dynamic programming, advanced Decomposition methods, Alternating Projections, Proximal and operator splitting methods, Convex relaxations and randomized methods for non-convex hard problems (including NP-Hard), Sparsity-based methods, Sequential convex programming, Branch-and-bound methods, Robust optimization. Selected applications in areas such as signal processing, data analytics, machine learning, control, circuit design, wireless communication & sensor networks, distributed data processing on graphs.

Læringsutbytte

Upon successful completion of this course, the students should:

- know how to recognize, model and formulate research problems from different fields as convex or non-convex optimization problems.

- understand the underlying theory, concepts and properties related to advanced optimization tools, especially those that are useful for computing solutions efficiently in both convex and non-convex problems.

- be able to design, implement and simulate practical algorithms to solve the various optimization problems.

- be able to analyze the structures of complex optimization problems and the corresponding exact or approximate solutions, as well as the relationships or approximations betwendifferent problems.

Vilkår for å gå opp til eksamen

Compulsory attendance is the only requirement.

Eksamen

pass (A or B) or fail. (based on 30% of Homework grade + 70% of Final Examination/Project grade).