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Beyond the Glass Box: Constraints as Objects
, 1995
"... Constraint Logic Programming (CLP) is a very active research area. One reason being that finite domain CLP systems have been successfully applied to various combinatorial optimization problems such as time tabling, scheduling, frequency allocation, manpower planning, production planning. State of th ..."
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Cited by 50 (2 self)
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of the art finite domain CLP languages offer programming constructs that gives access to the state of the constraint solver. With these constructs, new constraints can be defined in the CLP language directly, hence the name "glassbox". However, current glass box approaches do not give access
On nonconvex quadratic programming with box constraints
 SIAM J. on Optimiz
"... NonConvex Quadratic Programming with Box Constraints is a fundamental N Phard global optimisation problem. Recently, some authors have studied a certain family of convex sets associated with this problem. We prove several fundamental results concerned with these convex sets: we determine their dim ..."
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Cited by 19 (2 self)
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NonConvex Quadratic Programming with Box Constraints is a fundamental N Phard global optimisation problem. Recently, some authors have studied a certain family of convex sets associated with this problem. We prove several fundamental results concerned with these convex sets: we determine
Box constraint collections for adhoc constraints
 In Proceedings of the 9th International Conference on Principles and Practice of Constraint Programming
, 2003
"... ..."
CONVEX SEPARABLE PROBLEMS WITH LINEAR AND BOX CONSTRAINTS
, 2014
"... In this work, we focus on separable convex optimization problems with linear and box constraints and compute the solution in closedform as a function of some Lagrange multipliers that can be easily computed in a finite number of iterations. This allows us to bridge the gap between a wide family of ..."
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In this work, we focus on separable convex optimization problems with linear and box constraints and compute the solution in closedform as a function of some Lagrange multipliers that can be easily computed in a finite number of iterations. This allows us to bridge the gap between a wide family
Sparse Least Squares Problems with Box Constraints
"... In this thesis the sparse least squares problem with box constraints is considered. This problem has the form min lxu kAx \Gamma bk 2 , where some lower/upper bounds may not be present. This type of problem arises from, for example, reconstruction problems in geodesy and tomography. Here methods bas ..."
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Cited by 8 (0 self)
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In this thesis the sparse least squares problem with box constraints is considered. This problem has the form min lxu kAx \Gamma bk 2 , where some lower/upper bounds may not be present. This type of problem arises from, for example, reconstruction problems in geodesy and tomography. Here methods
A Least Square Kernel Machine with Box Constraints
"... In this paper, we present a least square kernel machine with box constraints (LSKMBC). The existing least square machines assume Gaussian hyperpriors and subsequently express the optima of the regularized squared loss as a set of linear equations. The generalized LASSO framework deviates from the as ..."
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Cited by 2 (0 self)
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In this paper, we present a least square kernel machine with box constraints (LSKMBC). The existing least square machines assume Gaussian hyperpriors and subsequently express the optima of the regularized squared loss as a set of linear equations. The generalized LASSO framework deviates from
JOINT BOXCONSTRAINT AND DEREGULARIZATION IN MULTIUSER DETECTION
"... Multiuser detection can be described as a quadratic optimization problem with binary constraint. Many techniques are available to find approximate solution to this problem. These techniques can be characterized in terms of complexity and detection performance. The “efficient frontier ” of known tech ..."
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in the nonstationary Tikhonov iterated algorithm. The deregularization maximizes the energy of the solution; this is opposite to the Tikhonov regularization where the energy is minimized. However, combined with boxconstraints, the deregularization forces the solution to be close to the binary set. Our development
A Least Square Kernel Machine With Box Constraints
, 2010
"... Principle of parsimony (Occams razor) is a key principle where the unnecessary complexity of a classier is regulated to improve the generalization performance in pattern classication. In decision tree construction, often the complexity is regu lated by early stopping the growth of a tree at a node ..."
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this heuristic namely, a least square kernel machine with box constraints (LSKMBC). In our approach, we consider uniform priors and obtain the loss functional for a given margin considered to be a model selection parameter. The framework not only di?ers from the existing least square kernel machines, but also
On The Maximization Of A Concave Quadratic Function With Box Constraints
, 1994
"... . We introduce a new method for maximizing a concave quadratic function with bounds on the variables. The new algorithm combines conjugate gradients with gradient projection techniques, as the algorithm of Mor'e and Toraldo (SIAM J. on Optimization 1, pp. 93113) and other wellknown methods do ..."
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Cited by 36 (12 self)
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function on a box) appears frequently in applications, for instance in finite difference discretization ...
UPPAAL in a Nutshell
, 1997
"... . This paper presents the overall structure, the design criteria, and the main features of the tool box Uppaal. It gives a detailed user guide which describes how to use the various tools of Uppaal version 2.02 to construct abstract models of a realtime system, to simulate its dynamical behavior, ..."
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Cited by 662 (51 self)
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. This paper presents the overall structure, the design criteria, and the main features of the tool box Uppaal. It gives a detailed user guide which describes how to use the various tools of Uppaal version 2.02 to construct abstract models of a realtime system, to simulate its dynamical behavior
Results 1  10
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177,854