Many existing distributed data mining algorithms do not allow users to express the patterns to be mined according to their intention via the use of constraints. Consequently, these unconstrained mining algorithms can yield numerous patterns that are not interesting to users. Moreover, due to inherited measurement inaccuracies and/or network latencies, data are often riddled with uncertainty. Th...

Efforts aimed at combining Operations Research and Constraint Programming have become increasingly prominent and successful in the last few years. It is now widely recognized that integrating inference in the form of constraint propagation and relaxation in the form of linear programming can yield substantial results. In this paper we argue the benefits of global constraints as a basis for such...

Absrmer-This paper presents a new approach to optimal design centering, the optimal assignment of paramete-r tolerances and the determination and optimization of production yield. Based upon muftidimensionaf linear cuts of the tolerance orthotope and uniform distributions of outcomes between tolerance extremes in the orthotope, exact formulas for yield and yield sensitivities, witb respect to d...

In this paper, a model of an optimal control problem with chance constraints is introduced. The parametersof the constraints are fuzzy, random or fuzzy random variables. Todefuzzify the constraints, we consider possibility levels.  Bychance-constrained programming the chance constraints are converted to crisp constraints which are neither fuzzy nor stochastic and then the resulting classical op...

In this paper, we develop a new approach for mechanizing induction on complex data structures (like bags, sorted lists, trees, powerlists. . . ) by adapting and generalizing works in tree automata with constraints. The key idea of our approach is to compute a tree grammar with constraints which describes the initial model of the given specification. This grammar is used as an induction schema f...

Integer Linear Programming has recently been used for decoding in a number of probabilistic models in order to enforce global constraints. However, in certain applications, such as non-projective dependency parsing and machine translation, the complete formulation of the decoding problem as an integer linear program renders solving intractable. We present an approach which solves the problem in...

A vast body of literature in combinatorics and computer science aims at understanding the structural properties of a poset P implied by placing certain marginal constraints on the uniform distribution over linear extensions of P . These questions are typically concerned with whether or not P must be a total order (or have small width). Here, we instead consider whether or not these marginal con...

We present the hybrid ASP solver clingcon, combining the simple modeling language and the high performance Boolean solving capacities of Answer Set Programming (ASP) with techniques for using non-Boolean constraints from the area of Constraint Programming (CP). The new clingcon system features an extended syntax supporting global constraints and optimize statements for constraint variables. The...

We present semidefinite relaxations for unconstrained nonconvex quadratic mixed-integer optimization problems. These relaxations yield tight bounds and are computationally easy to solve for mediumsized instances, even if some of the variables are integer and unbounded. In this case, the problem contains an infinite number of linear constraints; these constraints are separated dynamically. We us...