Optimization (B-KUL-H03E3A)
Aims
The course gives insight into the mathematical formulation of optimization problems and deals with advanced methods and algorithms to solve these problems. The knowledge of the possibilities and shortcomings of these algorithms should lead to a beter understanding of their applicability in solving concrete engineering problems. In the course, an overview of existing software for optimization will also be given, this software will be used in the practical exercise sessions. The student learns to select the appropriate solving methods and software for a wide range of optimization problems and learns to correctly interpret the results.
The following knowledge and skills will be acquired during this course:
- The student will be able to formulate a mathematical optimization problem starting from a concrete engineering problem.
- The student will be able to classify optimization problems into appropriate categories (e.g., convex vs. non-convex problems).
- The student will be familiar with different optimization strategies and their properties, and will hence be able to decide which strategy to use for a given optimization problem.
- The student will be able to formulate the optimality conditions for a given optimization problem.
- The student will have a profound understanding of a wide variety of optimization algorithms and their properties, and will be able to apply the appropriate algorithms for a given optimization problem.
- The student will be familiar with state-of-the-art optimization software packages, and will be able to use these in an efficient manner.
Previous knowledge
Skills: the student should be able to analyze, synthesize and interpret.
Knowledge: Analysis, Numerical mathematics, Numerical linear algebra.
Order of Enrolment
This course unit is a prerequisite for taking the following course units:
H0T46A : Project Mathematical Engineering
Identical courses
This course is identical to the following courses:
H0S15A : Optimalisatie
Is included in these courses of study
- Master of Bioinformatics (Leuven) (Bioscience Engineering) 120 ects.
- Master of Bioinformatics (Leuven) (Engineering) 120 ects.
- Master of Statistics and Data Science (on campus) (Leuven) (Theoretical Statistics and Data Science) 120 ects.
- Master of Engineering: Energy (Leuven) (Option: Electrical Energy) 120 ects.
- Master of Engineering: Energy (Leuven) (Option: General Techno-Economic Energy Knowledge) 120 ects.
- Master of Engineering: Energy (Leuven) (Option: Thermo-Mechanical Energy) 120 ects.
- Master of Mathematics (Leuven) 120 ects.
- Courses for Exchange Students Faculty of Engineering Science (Leuven)
- Master of Mathematical Engineering (Leuven) 120 ects.
- EIT-KIC Master in Energy (Leuven et al) (Option: Energy for Smart Cities) 120 ects.
- EIT-KIC Master in Energy (Leuven et al) (Option: Smart Electrical Networks and Systems (SENSE)) 120 ects.
- Master of Mobility and Supply Chain Engineering (Leuven) 120 ects.
- Master of Electrical Engineering (Leuven) (Information Systems and Signal Processing) 120 ects.
- Master of Electrical Engineering (Leuven) (Power Systems and Automation) 120 ects.
- Master of Civil Engineering (Leuven) 120 ects.
- Master in de ingenieurswetenschappen: bouwkunde (Leuven) 120 ects.
Activities
4 ects. Optimization: Lecture (B-KUL-H03E3a)
Content
1. Introduction
- a number of motivating examples (control, fitting, planning)
- mathematical modelling of optimization problems
- the importance of convexity
- classification of optimization problems
2. Algorithms for continuous optimization without constraints
- the two basic strategies: line search or trust region techniques
- gradient-based techniques: the steepest gradient and the added gradient method
- Newton and quasi-Newton techniques
- special methods for non-linea least square problems
3. Algorithms for continuous optimization with constraints
- the KKT-optimization conditions
- algorithms for linear problems: simplex-method and primal-dual interior point method
- algorithms for quadratic problems: active-set technique and interior point method
- convex optimization: formulation, the concept duality, algorithms
- general non-linear optimization (penalizing and barrier techniques, connection to interior point algorithms)
4. Introduction to global optimization methods
- deterministic methods (branch and bound, ...)
- stochastic and heuristic methods (Monte Carlo methods, simulated annealing, evolutionary algorithms, swarm-based algorithms,...)
5. Software
- discussion of the possibilities of the most current optimization software-packages
- sources on the internet: the Network Enabled Optimization Server
Course material
Study cost: 1-10 euros (The information about the study costs as stated here gives an indication and only represents the costs for purchasing new materials. There might be some electronic or second-hand copies available as well. You can use LIMO to check whether the textbook is available in the library. Any potential printing costs and optional course material are not included in this price.)
- Numerical Optimization, J. Nocedal and S. Wright, Springer, New York, 1999.
- Optimization Software Guide, J. Moré and S. Wright, SIAM, Philadelphia, 1993.
Is also included in other courses
2 ects. Optimization: Exercises and Laboratory Sessions (B-KUL-H03E4a)
Content
Exercises and lab sessions with the course Optimisation
Evaluation
Evaluation: Optimization (B-KUL-H23E3a)
Explanation
- part I, theory (closed-book with use of formulary)
- part II, exercises (Open-book on computer; example programs are available)