<jupyter_start><jupyter_text>TIES483 Nonlinear optimization* 5 credits* First Spring Term 2022* Postdoctoral researcher <NAME> (<EMAIL>)* Doctoral student <NAME> (<EMAIL>) General informationLevel: Master level course in mathematical information technology (*Tietotekniikka in Finnish*). Semi mandatory course for MSc studies in computational sciences.  Contents Optimization of functions of a single variable (line search). Optimization of functions with several variables with and without constraints. Optimality conditions. Software for nonlinear optimization. Some applications of optimization.This course deals with methods that mostly can be classified as* local optimization methods, which converge to the nearest local optimum* point-by-point i.e., non-population based (population based methods are taught on *TIES451 Selected topics in soft computing*)* single-objective (more multiobjective is taught on *TIES598 Nonlinear Multiobjective Optimization*)* based on a continuous mathematical model of the phenomena (data-based optimization aka *prescriptive analytics* is taught on *TIES583 Advanced Course on Optimization*)  Prerequisites * Basic knowledge on optimization (e.g. course TIEA382 Linear and discrete optimization) and numerical methods as well as basic programming skills.* Basic knowledge on mathematical analysis of functions of multiple variables. Teaching* Lectures (in English): 36 h, Mon 12-14 and Wed 14-16 in ZOOM 10.1.-2.3.2022* Exercise feedback sessions (demos): 14h, Thu 14-16 in ZOOM 20.1.-3.3.2022  Passing the courseDuring this course you will be given* a number of excercises (one each week) and* one slightly bigger assignment towards the end of the course.Excercises will comprise **70%** of the points and the assignment will comprise **30%** of the points. We will use the following grading:* 90% or more of the points -> grade 5,* 80% or more of the points -> grade 4,* 70% or more of the points -> grade 3,* 60% or more of the points -> grade 2,* 50% or more of the points -> grade 1, and* Less than 50% of the points -> failed.  How demanding the course is?5 credits → 5*26 = 130 hours of work* Lectures: 16*2 = 32 hours* Exercise feedback sessions: 7*2 = 14 hours* Self study (doing exercises, assignment, studying): 130 - 32 – 14 = 84 hours  Course plan**Introduction***   Lecture 1. Basic information about the course and study methods (Python, Jupyter etc.)*   Lecture 2. The very basics: what is an optimization problem, how to solve one, and line search    * I will inform you about the topics of the coming week’s lectures in advance    * You can also find the updates in https://moodle.jyu.fi/course/view.php?id=16603&section=1tabs-tree-start   Learning outcomes* Recognize and solve different types of optimization problems* Understand the basic approaches in solving nonlinear optimization problems * Recognize optimality conditions for unconstrained and constrained problems and learn how to apply them to verify optimality of a solution* Understand the basics of choosing and implementing optimization methods* Learn how to search and apply optimization software in order to solve nonlinear optimization problems* Understand differences in solving convex and non-convex optimization problems * Recognize basic ideas of solving multiobjective optimization problems  Let’s introduce ourselves* Who are you and what are you studying?* Master or PhD student?* Previous experiences of optimization?* How is this course related to your studies/research?* What are your expectations about this course?*Please use camera as well so we can see who is introducing her/himself!*  Study methods Python and JupyterCourtesy of Jussi Hakanen & <NAME>ainenAs the programming language Python 3, is used on this course. The course is taught using Jupyter notebooks (http://ipython.org/notebook.html, formerly known as IPython notebooks). You can study basics of Python from e.g. https://github.com/jrjohansson/scientific-python-lectures (at least lectures 0-2) if you are not familiar with Python. Github The source code of the Jupyter notebooks thought in this course will be available at Github at https://github.com/bshavazipour/TIES483-2022. ZoomAs you know now, the lectures will be given via Zoom. We will see if we need to switch to contact teaching later.  MoodleYour answers to the weekly exercises and the assignment during the course are submitted to Moodle (https://moodle.jyu.fi/course/view.php?id=16603). You will also get the feedback there. Mailing listThe mailing list  <EMAIL> is used to distribute information. The students acan also use that to post questions that could be helpful for others.  LiverevealThe Jupyter notebooks will be transferred to slides using https://github.com/damianavila/RISE. To view the contents at home, you do not need to have this installed. How to get scroll bar to slides?After installing rise (https://github.com/damianavila/RISE)* restart the notebook, and you should get a scroll bar; if not, try the following:* go to "Python installation dir"\share\jupyter\nbextensions\rise * open main.js (with some text editor) * replace 'scroll: false' with 'scroll: true' * restart the notebook and you should get a scroll bar<jupyter_code>from IPython.core.display import HTML
HTML("<style>.container { width:95% !important; }</style>")<jupyter_output><empty_output>
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