Generalized local propagation: A framework for solving constraint hierarchies

Hosobe, Hiroshi; Matsuoka, Satoshi; Yonezawa, Akinori

doi:10.1007/3-540-61551-2_78

Cited by 18 publications

(4 citation statements)

References 10 publications

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“…Indigo [Borning et al 1996] handles acyclic collections of inequality constraints, but not cycles (simultaneous equations and inequalities). User interface systems that handle simultaneous linear equations include DETAIL [Hosobe et al 1996] and Ultraviolet [Borning and FreemanBenson 1998]. A number of researchers (including the second author) have experimented with a straightforward use of a simplex package in a UI constraint solver, but the speed was not satisfactory for interactive use.…”

Section: Related Workmentioning

confidence: 99%

The Cassowary linear arithmetic constraint solving algorithm

Badros

Borning

Stuckey

2001

ACM Trans. Comput.-Hum. Interact.

144

128

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Linear equality and inequality constraints arise naturally in specifying many aspects of user interfaces, such as requiring that one window be to the left of another, requiring that a pane occupy the leftmost third of a window, or preferring that an object be contained within a rectangle if possible. Previous constraint solvers designed for user interface applications cannot handle simultaneous linear equations and inequalities efficiently. This is a major limitation, as such systems of constraints arise often in natural declarative specifications. We describe Cassowaryan incremental algorithm based on the dual simplex method, which can solve such systems of constraints efficiently. We have implemented the algorithm as part of a constraint-solving toolkit. We discuss the implementation of the toolkit, its application programming interface, and its performance.

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Section: Related Workmentioning

confidence: 99%

The Cassowary linear arithmetic constraint solving algorithm

Badros

Borning

Stuckey

2001

ACM Trans. Comput.-Hum. Interact.

144

128

View full text Add to dashboard Cite

show abstract

“…The Indigo algorithm [29] uses interval propagation to solve acyclic collections of inequality constraints, however, it does not handle simultaneous equality and inequality constraints. This is overcome by the Detail method [30,31], which can solve linear equality and inequality constraints simultaneously.…”

Section: Related Workmentioning

confidence: 99%

Kaczmarz Algorithm with Soft Constraints for User Interface Layout

Jamil

Needell

Müller

et al. 2013

2013 IEEE 25th International Conference on Tools With Artificial Intelligence

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ABSTRACT. The Kaczmarz method is an iterative method for solving large systems of equations that projects iterates orthogonally onto the solution space of each equation. In contrast to direct methods such as Gaussian elimination or QRfactorization, this algorithm is efficient for problems with sparse matrices, as they appear in constraint-based user interface (UI) layout specifications. However, the Kaczmarz method as described in the literature has its limitations: it considers only equality constraints and does not support soft constraints, which makes it inapplicable to the UI layout problem.In this paper we extend the Kaczmarz method for solving specifications containing soft constraints, using the prioritized IIS detection algorithm. Furthermore, the performance and convergence of the proposed algorithms are evaluated empirically using randomly generated UI layout specifications of various sizes. The results show that these methods offer improvements in performance over standard methods like Matlab's LINPROG, a well-known efficient linear programming solver.

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“…Newer constraint programming languages are AKL [46], cc(FD) [47], CIAO [48], DE-TAIL [49], Prolog IV [50], Eclipse [51], Oz [52,29,53], HAL [54] and SALSA [55]. Their constraint vocabulary and solvers go beyond traditional linear and non-linear constraints and support logical and global constraints.…”

Section: Literature Reviewmentioning

confidence: 99%

Using UML and OCL for representing multiobjective combinatorial optimization problems

Eracar

Kokar

2012

J Intell Manuf

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This paper describes the results of a preliminary feasibility study of an approach to representing Multiobjective Combinatorial Optimization Problems in UML (structural constraints) and OCL (procedural constraints) and then automatically translating the representations to a Constraint Satisfaction solving language (Oz) for execution. The paper presents two examples of the application of the approach-a job scheduling problem and a (fixture) design problem. The main goal of this paper is to investigate directions towards a standard, graphical language for representing combinatorial optimization problems. The paper shows that for the two selected problems it is easy to represent structural constraints in UML and that procedural constraints are representable in OCL. The results also show that a developed translator automatically converts the UML/OCL representations to Oz and that the resulting Oz program performs very reasonably, in some cases outperforming the handwritten benchmark programs.

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Generalized local propagation: A framework for solving constraint hierarchies

Cited by 18 publications

References 10 publications

The Cassowary linear arithmetic constraint solving algorithm

The Cassowary linear arithmetic constraint solving algorithm

Kaczmarz Algorithm with Soft Constraints for User Interface Layout

Using UML and OCL for representing multiobjective combinatorial optimization problems

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