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25
Solving Polynomial Systems Using a Branch and Prune Approach
 SIAM Journal on Numerical Analysis
, 1997
"... This paper presents Newton, a branch & prune algorithm to find all isolated solutions of a system of polynomial constraints. Newton can be characterized as a global search method which uses intervals for numerical correctness and for pruning the search space early. The pruning in Newton consists ..."
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Cited by 112 (7 self)
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This paper presents Newton, a branch & prune algorithm to find all isolated solutions of a system of polynomial constraints. Newton can be characterized as a global search method which uses intervals for numerical correctness and for pruning the search space early. The pruning in Newton consists in enforcing at each node of the search tree a unique local consistency condition, called boxconsistency, which approximates the notion of arcconsistency wellknown in artificial intelligence. Boxconsistency is parametrized by an interval extension of the constraint and can be instantiated to produce the HansenSegupta's narrowing operator (used in interval methods) as well as new operators which are more effective when the computation is far from a solution. Newton has been evaluated on a variety of benchmarks from kinematics, chemistry, combustion, economics, and mechanics. On these benchmarks, it outperforms the interval methods we are aware of and compares well with stateoftheart continuation methods. Limitations of Newton (e.g., a sensitivity to the size of the initial intervals on some problems) are also discussed. Of particular interest is the mathematical and programming simplicity of the method.
Interval arithmetic: From principles to implementation
 J. ACM
"... We start with a mathematical definition of a real interval as a closed, connected set of reals. Interval arithmetic operations (addition, subtraction, multiplication and division) are likewise defined mathematically and we provide algorithms for computing these operations assuming exact real arithme ..."
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Cited by 96 (12 self)
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We start with a mathematical definition of a real interval as a closed, connected set of reals. Interval arithmetic operations (addition, subtraction, multiplication and division) are likewise defined mathematically and we provide algorithms for computing these operations assuming exact real arithmetic. Next, we define interval arithmetic operations on intervals with IEEE 754 floating point endpoints to be sound and optimal approximations of the real interval operations and we show that the IEEE standard’s specification of operations involving the signed infinities, signed zeros, and the exact/inexact flag are such as to make a correct and optimal implementation more efficient. From the resulting theorems we derive data that are sufficiently detailed to convert directly to a program for efficiently implementing the interval operations. Finally we extend these results to the case of general intervals, which are defined as connected sets of reals that are not necessarily closed. 1
Interval arithmetic yields efficient dynamic filters for computational geometry
 Disc. Appl. Maths
"... We discuss floatingpoint filters as a means of restricting the precision needed for arithmetic operations while still computing the exact result. We show that interval techniques can be used to speed up the exact evaluation of geometric predicates and describe an efficient implementation of interva ..."
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Cited by 66 (13 self)
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We discuss floatingpoint filters as a means of restricting the precision needed for arithmetic operations while still computing the exact result. We show that interval techniques can be used to speed up the exact evaluation of geometric predicates and describe an efficient implementation of interval arithmetic that is strongly influenced by the rounding modes of the widely used IEEE 754 standard. Using this approach we engineer an efficient floatingpoint filter for the computation of the sign of a determinant that works for arbitrary dimensions. We validate our approach experimentally, comparing it with other static, dynamic and semistatic filters. 1
Interval Arithmetic: An Efficient Implementation And An Application To Computational Geometry
 In Workshop on Applications of Interval Analysis to systems and Control
, 1999
"... We discuss interval techniques for speeding up the exact evaluation of geometric predicates and describe a C++ implementation of interval arithmetic that is strongly influenced by the rounding modes of the widely used IEEE 754 standard. Using this approach we engineer an efficient floating point fil ..."
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Cited by 19 (1 self)
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We discuss interval techniques for speeding up the exact evaluation of geometric predicates and describe a C++ implementation of interval arithmetic that is strongly influenced by the rounding modes of the widely used IEEE 754 standard. Using this approach we engineer an efficient floating point filter for the computation of geometric predicates. We validate our approach experimentally, comparing it with other static, dynamic and semistatic filters.
A New Verified Optimization Technique For The "Packing Circles In A Unit Square" Problems
 SIAM Journal on Optimization
, 1994
"... The paper presents a new verified optimization method for the problem of finding the densest packing of nonoverlapping equal circles within a square. In order to provide reliable numerical results, the developed algorithm is based on interval analysis. As one of the most efficient parts of the algo ..."
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Cited by 18 (5 self)
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The paper presents a new verified optimization method for the problem of finding the densest packing of nonoverlapping equal circles within a square. In order to provide reliable numerical results, the developed algorithm is based on interval analysis. As one of the most efficient parts of the algorithm, an intervalbased version of a previous elimination procedure is introduced. This method represents the remaining areas still of interest as polygons fully calculated in a reliable way. The most promising strategy of finding optimal circle packing configurations is currently the partitioning of the original problem into subproblems. Still as a result of the highly increasing number of subproblems, earlier computeraided methods were not able to solve problem instances where the number of circles was greater than 27. The present paper provides a carefully developed technique resolving this difficulty by eliminating large groups of subproblems together. As a demonstration of the capabilities of the new algorithm the problems of packing 28, 29, and 30 circles were solved within very tight tolerance values. Our verified procedure decreased the uncertainty in the location of the optimal packings by more than 700 orders of magnitude in all cases.
CLIP: A CLP(Intervals) dialect for Metalevel Constraint Solving
 Practical Aspects of Declarative Languages: PADL2000, volume 1753 of LNCS
, 2000
"... CLIP is an implementation of CLP(Intervals) built on top of Prolog. It has been developed using the simplest model of constraint solving in which constraints are decomposed into sets of primitive constraints and a simple constraint queue is used to repeatedly apply primitive constraint contractions ..."
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Cited by 13 (3 self)
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CLIP is an implementation of CLP(Intervals) built on top of Prolog. It has been developed using the simplest model of constraint solving in which constraints are decomposed into sets of primitive constraints and a simple constraint queue is used to repeatedly apply primitive constraint contractions until some termination condition applies (e.g. no significant change occurs, or a preset limit on the number of contractions is reached). The simple semantics and implementation make it relatively straightforward to prove that the system is sound. In this paper we show how to implement more complex constraint solvers in CLIP. In particular, we demonstrate that several of the boxnarrowing algorithms from the Newton and Numerica systems can be easily implemented in CLIP. The principal advantages of this approach are (1) the resulting solvers are relatively easy to prove correct, (2) new solvers can be rapidly prototyped since the code is more concise and declarative than for imperative languages, and (3) contractors can be implemented directly from mathematical formulae without having to first prove results about interval arithmetic operators.
Global optimization in geometry — Circle packing into the square
 Essays and Surveys in Global Optimization
, 2005
"... ..."
New Interval Methods for Constrained Global Optimization
"... Interval analysis is a powerful tool which allows to design branchandbound algorithms able to solve many global optimization problems. In this paper we present new adaptive multisection rules which enable the algorithm to choose the proper multisection type depending on simple heuristic decision r ..."
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Cited by 12 (3 self)
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Interval analysis is a powerful tool which allows to design branchandbound algorithms able to solve many global optimization problems. In this paper we present new adaptive multisection rules which enable the algorithm to choose the proper multisection type depending on simple heuristic decision rules. Moreover, for the selection of the next box to be subdivided, we investigate new criteria. Both the adaptive multisection and the subinterval selection rules seem to be specially suitable for being used in inequality constrained global optimization problems. The usefulness of these new techniques is shown by computational studies.
Metalevel Interval Arithmetic and Verifiable Constraint Solving
, 2001
"... CLIP is an implementation of CLP(Intervals) which has been designed to be verifiably correct in the sense that the answers it returns are mathematically correct solutions to the underlying arithmetic constraints. This fundamental design criteria affects many aspects of the implementation from the in ..."
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Cited by 10 (2 self)
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CLIP is an implementation of CLP(Intervals) which has been designed to be verifiably correct in the sense that the answers it returns are mathematically correct solutions to the underlying arithmetic constraints. This fundamental design criteria affects many aspects of the implementation from the input and output of decimal constants to the design of the interval arithmetic libraries and the constraint solving algorithms. In particular, to enhance verifiability, CLIP employs the simplest model of constraint solving in which constraints are decomposed into sets of primitive constraints which are then solved using a library of primitive constraint contractors. This approach results in a simple constraint solver whose correctness is relatively straightforward to verify, but the solver is only able to solve relatively simple constraints. In this paper, we present the syntax, semantics, and implementation of CLIP, and we show how to use metalevel techniques to enhance the power of the CLIP constraint solver while preserving the simple structure of the system. In particular, we demonstrate that several of the boxnarrowing algorithms from the Newton and Numerica systems can be easily implemented in CLIP. The principal advantages of this approach are (1) the resulting solvers are relatively easy to prove correct, (2) new solvers can be rapidly prototyped since the code is more concise and declarative than for imperative languages, and (3) contractors can be implemented directly from mathematical formulae without having to first prove results about interval arithmetic operators. Finally, the source code for the system is publicly available, which is a clear prerequisite for public, independent verifiability.