Results 1 - 10 of 1,659

Constrained model predictive control: Stability and optimality

by D. Q. Mayne, J. B. Rawlings, C. V. Rao, P. O. M. Scokaert - AUTOMATICA , 2000
"... Model predictive control is a form of control in which the current control action is obtained by solving, at each sampling instant, a finite horizon open-loop optimal control problem, using the current state of the plant as the initial state; the optimization yields an optimal control sequence and t ..."
Abstract - Cited by 738 (16 self) - Add to MetaCart
from an extensive literature essential principles that ensure stability and use these to present a concise characterization of most of the model predictive controllers that have been proposed in the literature. In some cases the finite horizon optimal control problem solved on-line is exactly

Near-optimal sensor placements in gaussian processes

by Andreas Krause, Ajit Singh, Carlos Guestrin, Chris Williams - In ICML , 2005
"... When monitoring spatial phenomena, which can often be modeled as Gaussian processes (GPs), choosing sensor locations is a fundamental task. There are several common strategies to address this task, for example, geometry or disk models, placing sensors at the points of highest entropy (variance) in t ..."
Abstract - Cited by 342 (34 self) - Add to MetaCart
) in the GP model, and A-, D-, or E-optimal design. In this paper, we tackle the combinatorial optimization problem of maximizing the mutual information between the chosen locations and the locations which are not selected. We prove that the problem of finding the configuration that maximizes mutual

Exact algorithms for NP-hard problems: a survey, in: Combinatorial Optimization—Eureka, you shrink!”,

by Gerhard J Woeginger - Lecture Notes in Computer Science, , 2003
"... Abstract. We discuss fast exponential time solutions for NP-complete problems. We survey known results and approaches, we provide pointers to the literature, and we discuss several open problems in this area. The list of discussed NP-complete problems includes the travelling salesman problem, sched ..."
Abstract - Cited by 151 (4 self) - Add to MetaCart
Abstract. We discuss fast exponential time solutions for NP-complete problems. We survey known results and approaches, we provide pointers to the literature, and we discuss several open problems in this area. The list of discussed NP-complete problems includes the travelling salesman problem

CABOB: A fast optimal algorithm for combinatorial auctions

by Tuomas Sandholm, Subhash Suri, Andrew Gilpin, David Levine
"... Combinatorial auctions where bidders can bid on bundles of items can lead to more economical allocations, but determining the winners is-complete and inapproximable. We present CABOB, a sophisticated search algorithm for the problem. It uses decomposition techniques, upper and lower bounding (also a ..."
Abstract - Cited by 137 (26 self) - Add to MetaCart
Combinatorial auctions where bidders can bid on bundles of items can lead to more economical allocations, but determining the winners is-complete and inapproximable. We present CABOB, a sophisticated search algorithm for the problem. It uses decomposition techniques, upper and lower bounding (also

On the complexity of local search

by David S. Johnson, Christos H. Papadimitriou - Proc. of 22nd ACM Symp. on Theory of Computing (STOC , 1990
"... We investigate the complexity of finding locally optimal solutions to NP-hard com-binatorial optimization problems. Local optimality arises in the context of local search algorithms, which try to find improved solutions by considering perturbations of the current solution (“neighbors ” of that solut ..."
Abstract - Cited by 229 (9 self) - Add to MetaCart
We investigate the complexity of finding locally optimal solutions to NP-hard com-binatorial optimization problems. Local optimality arises in the context of local search algorithms, which try to find improved solutions by considering perturbations of the current solution (“neighbors

Learning the Empirical Hardness of Optimization Problems: The case of combinatorial auctions

by Kevin Leyton-Brown, Eugene Nudelman, Yoav Shoham - In CP , 2002
"... We propose a new approach to understanding the algorithm-specific empirical hardness of optimization problems. In this work we focus on the empirical hardness of the winner determination problem---an optimization problem arising in combinatorial auctions---when solved by ILOG's CPLEX software. ..."
Abstract - Cited by 78 (23 self) - Add to MetaCart
We propose a new approach to understanding the algorithm-specific empirical hardness of optimization problems. In this work we focus on the empirical hardness of the winner determination problem---an optimization problem arising in combinatorial auctions---when solved by ILOG's CPLEX software

Heuristics From Nature For Hard Combinatorial Optimization Problems

by A. Colorni, M. Dorigo, F. Maffioli, V. Maniezzo, G. Righini, M. Trubian , 1996
"... In this paper we try to describe the main characters of Heuristics "derived" from Nature, a border area between Operations Research and Artificial Intelligence, with applications to graph optimization problems. These algorithms take inspiration from physics, biology, social sciences, and u ..."
Abstract - Cited by 33 (0 self) - Add to MetaCart
. The paper is then composed of six review sections: each of them concerns a heuristic and its application to an NP-hard combinatorial optimization problem. We consider the following topics: genetic algorithms with timetable problems, simulated annealing with dial-a-ride problems, sampling & clustering

Semidefinite optimization

by M. J. Todd - Acta Numerica , 2001
"... Optimization problems in which the variable is not a vector but a symmetric matrix which is required to be positive semidefinite have been intensely studied in the last ten years. Part of the reason for the interest stems from the applicability of such problems to such diverse areas as designing the ..."
Abstract - Cited by 152 (2 self) - Add to MetaCart
the strongest column, checking the stability of a differential inclusion, and obtaining tight bounds for hard combinatorial optimization problems. Part also derives from great advances in our ability to solve such problems efficiently in theory and in practice (perhaps “or ” would be more appropriate: the most

Regret in online combinatorial optimization.

by Jean-Yves Audibert , Sébastien Bubeck , Gábor Lugosi - Mathematics of Operations Research, , 2014
"... Abstract We address online linear optimization problems when the possible actions of the decision maker are represented by binary vectors. The regret of the decision maker is the difference between her realized loss and the best loss she would have achieved by picking, in hindsight, the best possib ..."
Abstract - Cited by 15 (1 self) - Add to MetaCart
Abstract We address online linear optimization problems when the possible actions of the decision maker are represented by binary vectors. The regret of the decision maker is the difference between her realized loss and the best loss she would have achieved by picking, in hindsight, the best

Dynamic and Aggressive Scheduling Techniques for Power-Aware Real-Time Systems

by Hakan Aydin, Rami Melhem, Daniel Mossé, Pedro Mejía-Alvarez , 2001
"... In this paper, we address power-aware scheduling of periodic hard real-time tasks using dynamic voltage scaling. Our solution includes three parts: (a) a static (off-line) solution to compute the optimal speed, assuming worst-case workload for each arrival, (b) an on-line speed reduction mechanism t ..."
Abstract - Cited by 204 (24 self) - Add to MetaCart
In this paper, we address power-aware scheduling of periodic hard real-time tasks using dynamic voltage scaling. Our solution includes three parts: (a) a static (off-line) solution to compute the optimal speed, assuming worst-case workload for each arrival, (b) an on-line speed reduction mechanism
Results 1 - 10 of 1,659