Machine learning methods and optimization methods can be combined in manifold ways. In this seminar, we concentrate on the use of machine learning techniques to accelerate already existing optimization algorithms, in particular solvers for mixed-integer (linear) programs.
Which subroutines of well-known optimization algorithms can profit from an application machine learning? Which machine learning algorithms are best suited to support decision making within optimization algorithms? Which problem classes benefit from combined approaches? Those question have been studied and partially answered in the literature of the past five years. Some highlights of recent research results are reviewed and we gain some insight into the current developments in this field.
<p>The seminar will be organized as a block seminar.</p>
<p>Location and schedule:</p>
<p>· first meeting (introduction and paper assignment): October 23, 16:00, ZIB (Takustr. 7), Seminar room <span style="color:windowtext">3028 </span> (first floor)</p>
<p>· second meeting (short talk): TBD, one day in midterm, at ZIB, about two hours</p>
<p>· final meeting (seminar talk): TBD, one or two full days at the end of the semester</p>
<p>Students should have basic knowledge in mathematical optimization.</p>
<p>At the second meeting, you are supposed to give a short, introductory talk (at most 5 minutes) on your topic.</p>
<p>To obtain credit points, you are required to hand in a short summary of your talk (LaTeX, 5 pages). The summary has to be sent to your advisor by E-mail two weeks before the third meeting. It will be graded and handed back to you before the final meeting in order to provide a first feedback.</p>
<p>The seminar itself will take place on one or two days (depending on the number of attendees) in the last weeks of the semester. Talks should be prepared for 45 minutes, such that 15 minutes remain for questioning and discussions. Having submitted a summary is a requirement for giving the final presentation.</p>
<p>Your final grade will be composed of 60% and 40% from the evaluation of your talk and summary, respectively.</p>