291 research outputs found
Contributions to complementarity and bilevel programming in Banach spaces
In this thesis, we derive necessary optimality conditions for bilevel programming problems (BPPs for short) in Banach spaces. This rather abstract setting reflects our desire to characterize the local optimal solutions of hierarchical optimization problems in function spaces arising from several applications.
Since our considerations are based on the tools of variational analysis introduced by Boris Mordukhovich, we study related properties of pointwise defined sets in function spaces. The presence of sequential normal compactness for such sets in Lebesgue and Sobolev spaces as well as the variational geometry of decomposable sets in Lebesgue spaces is discussed.
Afterwards, we investigate mathematical problems with complementarity constraints (MPCCs for short) in Banach spaces which are closely related to BPPs. We introduce reasonable stationarity concepts and constraint qualifications which can be used to handle MPCCs. The relations between the mentioned stationarity notions are studied in the setting where the underlying complementarity cone is polyhedric. The results are applied to the situations where the complementarity cone equals the nonnegative cone in a Lebesgue space or is polyhedral.
Next, we use the three main approaches of transforming a BPP into a single-level program (namely the presence of a unique lower level solution, the KKT approach, and the optimal value approach) to derive necessary optimality conditions for BPPs. Furthermore, we comment on the relation between the original BPP and the respective surrogate problem.
We apply our findings to formulate necessary optimality conditions for three different classes of BPPs. First, we study a BPP with semidefinite lower level problem possessing a unique solution. Afterwards, we deal with bilevel optimal control problems with dynamical systems of ordinary differential equations at both decision levels. Finally, an optimal control problem of ordinary or partial differential equations with implicitly given pointwise state constraints is investigated
Data-driven Inverse Optimization with Imperfect Information
In data-driven inverse optimization an observer aims to learn the preferences
of an agent who solves a parametric optimization problem depending on an
exogenous signal. Thus, the observer seeks the agent's objective function that
best explains a historical sequence of signals and corresponding optimal
actions. We focus here on situations where the observer has imperfect
information, that is, where the agent's true objective function is not
contained in the search space of candidate objectives, where the agent suffers
from bounded rationality or implementation errors, or where the observed
signal-response pairs are corrupted by measurement noise. We formalize this
inverse optimization problem as a distributionally robust program minimizing
the worst-case risk that the {\em predicted} decision ({\em i.e.}, the decision
implied by a particular candidate objective) differs from the agent's {\em
actual} response to a random signal. We show that our framework offers rigorous
out-of-sample guarantees for different loss functions used to measure
prediction errors and that the emerging inverse optimization problems can be
exactly reformulated as (or safely approximated by) tractable convex programs
when a new suboptimality loss function is used. We show through extensive
numerical tests that the proposed distributionally robust approach to inverse
optimization attains often better out-of-sample performance than the
state-of-the-art approaches
Packing ellipsoids with overlap
The problem of packing ellipsoids of different sizes and shapes into an
ellipsoidal container so as to minimize a measure of overlap between ellipsoids
is considered. A bilevel optimization formulation is given, together with an
algorithm for the general case and a simpler algorithm for the special case in
which all ellipsoids are in fact spheres. Convergence results are proved and
computational experience is described and illustrated. The motivating
application - chromosome organization in the human cell nucleus - is discussed
briefly, and some illustrative results are presented
A Prescriptive Trilevel Equilibrium Model for Optimal Emissions Pricing and Sustainable Energy Systems Development
We explore the class of trilevel equilibrium problems with a focus on
energy-environmental applications. In particular, we apply this trilevel
framework to a power market model, exploring the possibilities of an
international policymaker in reducing emissions of the system. We present two
alternative solution methods for such problems and a comparison of the
resulting model sizes. The first method is based on a reformulation of the
bottom-level solution set, and the second one uses strong duality. The first
approach results in optimality conditions that are both necessary and
sufficient, while the second one results in a model with fewer constraints but
only sufficient optimality conditions. Using the proposed methods, we are able
to obtain globally optimal solutions for a realistic five-node case study
representing the Nordic countries and assess the impact of a carbon tax on the
electricity production portfolio.Comment: 21 pages, 5 figure
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