PhD Courses of interest for AIROYoungers

The aim of this section is to collect in one single page all incoming courses of interest for PhD students (not workshops or conferences, but lectures and webinars as well). So, if you know that at a certain University there will be a particular course addressed for PhD students, let us know to spread the word!

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Displaying 1 course
Start date End date Description Location Country Url
27 Oct 2020 29 Oct 2020 "Conic, especially copositive optimization" by Prof. I. M. Bomze

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Timetable: 8 hrs.
The course will be held online (link Zoom will be comunicated).

Calendar of the lectures

Tuesday October 27, 2020, 16:00
Wednesday October 28, 2020, 10:00-12:00
Thursday October 29, 2020, 10:00-12:00 and 15:00-17:00

Speaker: Prof. I. M. Bomze
Title: Conic, especially copositive optimization

Course contents.

Quite many combinatorial and some important non-convex continuous optimization
problems admit a conic representation, where the complexity of solving non-
convex programs is shifted towards the complexity of sheer feasibility (i.e.,
membership of the cone which is assumed to be a proper convex one), while
structural constraints and the objective are all linear. The resulting problem
is therefore a convex one, and still equivalent to some NP-hard problems with
inefficient local solutions despite the fact that in the conic formulation,
all local solutions are global.

Using characterizations of copositivity, one arrives at various
approximations. However, not all of these are tractable with current
technology. In this course, we will address some approaches on which tractable
SDP- or LP-approximations, and also branch-and-bound schemes, may be based.

This way, good tractable bounds can be achieved which serve as quality control
for any primal-feasible algorithm. But which one should be employed?
Complementing above (dual) approach, we will, mainly as one example, address a
classical yet not widely known first-order approach for poly/posynomial
optimization under simplex constraints, embedded in some general optimization
principles for iterative primal methods.

Online, University of Padova Italy Go