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A scheduling model for reduced CPU energy
 ANNUAL SYMPOSIUM ON FOUNDATIONS OF COMPUTER SCIENCE
, 1995
"... The energy usage of computer systems is becoming an important consideration, especially for batteryoperated systems. Various methods for reducing energy consumption have been investigated, both at the circuit level and at the operating systems level. In this paper, we propose a simple model of job s ..."
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Cited by 558 (3 self)
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scheduling aimed at capturing some key aspects of energy minimization. In this model, each job is to be executed between its arrival time and deadline by a single processor with variable speed, under the assumption that energy usage per unit time, P, is a convex function of the processor speed s. We give
PhD Position Nonconvex energy minimization by local decomposition
"... A large amount of computer vision and image processing problems is addressed by formulating an energy which measures the quality of a potential solution. One tries to minimize this energy, i.e. to find the configuration which corresponds to the lowest value of the energy. Several types of methods ca ..."
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can be used to reach the optimal solution of a nonconvex energy. Probabilistic algorithms [3,5,7] usually based on Monte Carlo sampling are well adapted for the complex configuration spaces (discrete and/or continuous). Some combinatorial methods [1,4,6] based on GraphCuts constitute efficient tools
ARTICLE NO. 71 Convex Energy Levels of Hamiltonian Systems
, 2004
"... To Professor Jorge Sotomayor for his 60 th birthday We give a simple necessary and sufficient condition for a nonregular energy level of a Hamiltonian system to be strictly convex. We suppose that the Hamiltonian function is given by kinetic plus potential energy. We also show that this condition h ..."
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To Professor Jorge Sotomayor for his 60 th birthday We give a simple necessary and sufficient condition for a nonregular energy level of a Hamiltonian system to be strictly convex. We suppose that the Hamiltonian function is given by kinetic plus potential energy. We also show that this condition
Strictly Proper Scoring Rules, Prediction, and Estimation
, 2007
"... Scoring rules assess the quality of probabilistic forecasts, by assigning a numerical score based on the predictive distribution and on the event or value that materializes. A scoring rule is proper if the forecaster maximizes the expected score for an observation drawn from the distribution F if he ..."
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Cited by 373 (28 self)
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attractive loss and utility functions that can be tailored to the problem at hand. This article reviews and develops the theory of proper scoring rules on general probability spaces, and proposes and discusses examples thereof. Proper scoring rules derive from convex functions and relate to information
Exact optimization for markov random fields with convex priors
 IEEE Transactions on Pattern Analysis and Machine Intelligence
, 2003
"... We introduce a method to solve exactly a first order Markov Random Field optimization problem in more generality than was previously possible. The MRF shall have a prior term that is convex in terms of a linearly ordered label set. The method maps the problem into a minimumcut problem for a direct ..."
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Cited by 216 (3 self)
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directed graph, for which a globally optimal solution can be found in polynomial time. The convexity of the prior function in the energy is shown to be necessary and sufficient for the applicability of the method.
Existence of multiple normal mode trajectories on convex energy surfaces of even classical Hamiltonian systems
 J. Differential Equations
, 1985
"... Hamiltonian systems of n degrees of freedom for which the Hamiltonian is a function that is even both in its joint n coordinate variables as well as in its joint n momentum variables are discussed. For such systems the number of distinct trajectories which correspond to particular periodic solution ..."
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Cited by 7 (0 self)
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solutions (normal modes) with the same energy, is investigated. To that end a constrained dual action principle is introduced. Applying minmax methods to this variational problem, several results are obtained, among which the existence of at least n distinct trajectories if specific conditions
A New Class of Upper Bounds on the Log Partition Function
 In Uncertainty in Artificial Intelligence
, 2002
"... Bounds on the log partition function are important in a variety of contexts, including approximate inference, model fitting, decision theory, and large deviations analysis [11, 5, 4]. We introduce a new class of upper bounds on the log partition function, based on convex combinations of distribution ..."
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Cited by 225 (32 self)
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of distributions in the exponential domain, that is applicable to an arbitrary undirected graphical model. In the special case of convex combinations of treestructured distributions, we obtain a family of variational problems, similar to the Bethe free energy, but distinguished by the following desirable
The concaveconvex procedure (CCCP)
, 2003
"... The ConcaveConvex procedure (CCCP) is a way to construct discrete time iterative dynamical systems which are guaranteed to monotonically decrease global optimization/energy functions. This procedure can be applied to almost any optimization problem and many existing algorithms can be interpreted ..."
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Cited by 70 (5 self)
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The ConcaveConvex procedure (CCCP) is a way to construct discrete time iterative dynamical systems which are guaranteed to monotonically decrease global optimization/energy functions. This procedure can be applied to almost any optimization problem and many existing algorithms can be interpreted
Evolution by NonConvex Functionals
, 2008
"... We establish a semigroup solution concept for morphological differential equations, such as the mean curvature flow equation. The proposed method consists in generating flows from generalized minimizers of nonconvex energy functionals. We use relaxation and convexification to define generalized min ..."
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minimizers. The main part of this work consists in verification of the solution concept by comparing analytical, rotationally invariant solutions of the mean curvature flow equation and iterative minimizer of a nonconvex energy functional. 1
Results 1  10
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