1,672 research outputs found
On the relationship between bilevel decomposition algorithms and direct interior-point methods
Engineers have been using bilevel decomposition algorithms to solve certain nonconvex large-scale optimization problems arising in engineering design projects. These algorithms transform the large-scale problem into a bilevel program with one upperlevel problem (the master problem) and several lower-level problems (the subproblems). Unfortunately, there is analytical and numerical evidence that some of these commonly used bilevel decomposition algorithms may fail to converge even when the starting point is very close to the minimizer. In this paper, we establish a relationship between a particular bilevel decomposition algorithm, which only performs one iteration of an interior-point method when solving the subproblems, and a direct interior-point method, which solves the problem in its original (integrated) form. Using this relationship, we formally prove that the bilevel decomposition algorithm converges locally at a superlinear rate. The relevance of our analysis is that it bridges the gap between the incipient local convergence theory of bilevel decomposition algorithms and the mature theory of direct interior-point methods
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Using EPECs to model bilevel games in restructured electricity markets with locational prices
CWPE0619 (EPRG0602) Xinmin Hu and Daniel Ralph (Feb 2006) Using EPECs to model bilevel games in restructured electricity markets with locational prices We study a bilevel noncooperative game-theoretic model of electricity markets with locational marginal prices. Each player faces a bilevel optimization problem that we remodel as a mathematical program with equilibrium constraints, MPEC. This gives an EPEC, equilibrium problem with equilibrium constraints. We establish sufficient conditions for existence of pure strategy Nash equilibria for this class of bilevel games and give some applications. We show by examples the effect of network transmission limits, i.e. congestion, on existence of equilibria. Then we study, for more general EPECs, the weaker pure strategy concepts of local Nash and Nash stationary equilibria. We model the latter via complementarity problems, CPs. Finally, we present numerical examples of methods that attempt to find local Nash or Nash stationary equilibria of randomly generated electricity market games. The CP solver PATH is found to be rather effective in this context
Inverse Optimization with Noisy Data
Inverse optimization refers to the inference of unknown parameters of an
optimization problem based on knowledge of its optimal solutions. This paper
considers inverse optimization in the setting where measurements of the optimal
solutions of a convex optimization problem are corrupted by noise. We first
provide a formulation for inverse optimization and prove it to be NP-hard. In
contrast to existing methods, we show that the parameter estimates produced by
our formulation are statistically consistent. Our approach involves combining a
new duality-based reformulation for bilevel programs with a regularization
scheme that smooths discontinuities in the formulation. Using epi-convergence
theory, we show the regularization parameter can be adjusted to approximate the
original inverse optimization problem to arbitrary accuracy, which we use to
prove our consistency results. Next, we propose two solution algorithms based
on our duality-based formulation. The first is an enumeration algorithm that is
applicable to settings where the dimensionality of the parameter space is
modest, and the second is a semiparametric approach that combines nonparametric
statistics with a modified version of our formulation. These numerical
algorithms are shown to maintain the statistical consistency of the underlying
formulation. Lastly, using both synthetic and real data, we demonstrate that
our approach performs competitively when compared with existing heuristics
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