Kursens beskrivning

LEARNING OUTCOMES

Students will be introduced to and trained to use the tools to recognize convex optimization problems that arise in engineering, scince, economics. They will be introduced to the basic theory of such problems, concentrating on results that are useful in computation. The will also be introduced to basic formats of convex optimization problems that are needed as an input form for convex optimization solvers, such as CVX, and will learn how to use CVX.

Credits: 5

Schedule: 15.09.2021 - 15.12.2021

Teacher in charge (valid for whole curriculum period):

Teacher in charge (applies in this implementation): Sergiy Vorobyov

Contact information for the course (applies in this implementation):

Prof. Sergiy A. Vorobyov

sergiy.vorobyov@aalto.fi

Dr. Sergiy Vorobyov (aalto.fi)

CEFR level (valid for whole curriculum period):

Language of instruction and studies (applies in this implementation):

Teaching language: English. Languages of study attainment: English

CONTENT, ASSESSMENT AND WORKLOAD

Content

• valid for whole curriculum period:

  Convex sets, functions, and optimization problems. Basics of convex analysis. Least-squares, linear and quadratic programs. Semidefinite programming. Solvers.

• applies in this implementation

  Catalog Description

  Concentrates on recognizing and solving (using standard packages) convex optimization problems that arise in practice!

  •        Convex sets, functions, and optimization problems.

  •        Least-squares, linear and quadratic programs.

  •        Semidefinite programming (SDP).

  •        Minimax, extremal volume, and other problems with geometric interpretation.

  •        Optimality conditions, duality theory, theorems of alternative.

  •        Introduction to unconstrained optimization algorithms.

  •        Introduction to interior-point methods for constrained optimization.

  •        Applications.

  
  

Assessment Methods and Criteria

• valid for whole curriculum period:

  Lectures, exercises, assignments.

• applies in this implementation

  •        4 homework assignments. Homeworks will normally be due in 2 weeks. Roughly 1 homework in every 2 weeks. The first week or 2 do not count since a proper introduction is first needed before we can start with homeworks. 

  •        Exam. The format will be decided depending on the situation. Currently, it is planned as in class exam.

  Grading: Homeworks: 50%. Exam: 50%. These weights are approximate. We reserve the right to change them later. Can be also discussed with you.

  
  

Workload

• valid for whole curriculum period:

  Lectures, excercises, and exams approximately 30 h, assignments and independent studying approximately 103 h, total 133 h

  Attendance in some contact teaching may be compulsory.

• applies in this implementation

  Follow approximately the standard workload of Lectures, excercises, and exams approximately 30 h (lectures and excercises are combined together), assignments and independent studying approximately 103 h, total 133 h. It will, however, slightly depend on individual training. 

DETAILS

Study Material

• applies in this implementation

  Textbook:

  Stephen Boyd; Lieven Vandenberghe, Convex Optimization
  

  https://web.stanford.edu/~boyd/cvxbook/bv_cvxbook.pdf
  

  
  Additional reading: 
  
  

  •        Ben-Tal and A. Nemirovski, Lectures on Modern Convex Optimization: Analysis, Algorithms, and Engineering Applications

  •        Y. Nesterov, Introductory Lectures on Convex Optimization: A Basic Course

  •        J. Gallier and J. Quaintance, Algebra, Topology, Differential Calculus, and Optimization Theory For Computer Science and Engineering

  
  

Substitutes for Courses

• valid for whole curriculum period:

  Substitutes for Courses

Prerequisites

• valid for whole curriculum period:

  Prerequisites

SDG: Sustainable Development Goals

9 Industry, Innovation and Infrastructure

FURTHER INFORMATION

Further Information

• valid for whole curriculum period:

  Teaching Period:

  2020-2021 Autumn I

  2021-2022 Autumn I - II

  Course Homepage: https://mycourses.aalto.fi/course/search.php?search=ELEC-E5422

  Registration for Courses: In the academic year 2021-2022, registration for courses will take place on Sisu (sisu.aalto.fi) instead of WebOodi.

  In WebOodi

• applies in this implementation

  Course Objectives

  •        to give the tools and training to recognize convex optimization problems that arise in electrical and computer engineering

  •        to present the basic theory of such problems, concentrating on results that are useful in computation

  •        to give a thorough understanding of how such problems are solved, and some experience in solving them

  •        to give the background required to use the standard methods and software packages in their own research work

  •        to give a number of examples of successful application of convex optimization techniques for solving problem in applied mathematics, computer science, statistics, electrical engineering, and science in general

  
  

Details on the schedule

• applies in this implementation

  Lectures combined with excercises take place in zoon every Wed. from 15.09.2021 to 15.12.2021. 

  The zoom room is opened from 9 to 11. 

  Lectures start at 9:15. 

  The break is approximately 15 mins. from 10 to 10:15. 

  The time from 9 to 9:15 can be used for organizational questions and other questions. 

  If additional consultations are needed, contact the teacher by email (see above).

  General questions that may interest everybody can be asked here in Mycourse dialog. 

Beskrivning