985 research outputs found
Global Solutions to Nonconvex Optimization of 4th-Order Polynomial and Log-Sum-Exp Functions
This paper presents a canonical dual approach for solving a nonconvex global
optimization problem governed by a sum of fourth-order polynomial and a
log-sum-exp function. Such a problem arises extensively in engineering and
sciences. Based on the canonical duality-triality theory, this nonconvex
problem is transformed to an equivalent dual problem, which can be solved
easily under certain conditions. We proved that both global minimizer and the
biggest local extrema of the primal problem can be obtained analytically from
the canonical dual solutions. As two special cases, a quartic polynomial
minimization and a minimax problem are discussed. Existence conditions are
derived, which can be used to classify easy and relative hard instances.
Applications are illustrated by several nonconvex and nonsmooth examples
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
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