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Advanced Algorithms and Heuristics for Solving Quantified
Mixed - Integer Linear Programs
Funded since 2018
Project Description:
Traditionally, it is assumed that the inputs of optimization
problems are predefined and well known at planning time.
However, considering uncertainty in the planning process is an
essential asset. There are various approaches in the literature,
how to deal with these uncertainties, one possibility is the use
of quantified mixed-integer linear programs.Quantified
mixed-integer linear programs are mixed integer linear programs
with variables being either existentially or universally
quantified. They can be interpreted as two-person zero- sum
games between an existential and a universal player on the one
side, or multistage optimization problems under uncertainty on
the other side. Solutions of quantified programs are so called
winning strategies for the existential player that specify how
to react on moves of the universal player – certain fixations of
universally quantified variables – to certainly win the
game.Long-term goal of our efforts is the development of a tool
for solving quantified mixed-integer linear programs and its
presentation to the the public, just in the spirit of Cplex,
Gurobi or Scip. In the pursuit of this objective, we develop,
refine and substantiate solution procedures for the mighty
modeling tool of quantified mixed-integer linear programs, in
order to apply it for practice relevant tasks. One step in this
direction was to publish the solver Yasol, as far as it exists
already. We hope and expect that the results of this project
will have far-reaching impact for research, as well as for
practical optimization applications. As a further significant
modeling extension, we will allow the active interference of
the uncertainty set.
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Downloads
Yasol for Mac OS
Yasol for Linux
Yasol for Windows
Instances
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