818 research outputs found
Analysis of nonlinear constraints in CLP(R)
Solving nonlinear constraints over real numbers is a complex problem. Hence constraint logic programming languages like CLP(R) or Prolog III solve only linear constraints and delay nonlinear constraints until they become linear. This efficient implementation method has the disadvantage that sometimes computed answers are unsatisfiable or infinite loops occur due to the unsatisfiability of delayed nonlinear constraints. These problems could be solved by using a more powerful constraint solver which can deal with nonlinear constraints like in RISC-CLP(Real). Since such powerful constraint solvers are not very efficient, we propose a compromise between these two extremes. We characterize a class of CLP(R) programs for which all delayed nonlinear constraints become linear at run time. Programs belonging to this class can be safely executed with the efficient CLP(R) method while the remaining programs need a more powerful constraint solver
A practical approach to the global analysis of CLP programs
This paper presents and illustrates with an example a practical approach to the dataflow analysis of programs written in constraint logic programming (CLP) languages using abstract interpretation. It is first argued that,
from the framework point of view, it sufnces to propose relatively simple extensions of traditional analysis methods which have already been proved useful and practical and for which efncient fixpoint algorithms have been
developed. This is shown by proposing a simple but quite general extensión of Bruynooghe's traditional framework to the analysis of CLP programs. In this extensión constraints are viewed not as "suspended goals" but rather as new information in the store, following the traditional view of CLP. Using this approach, and as an example of its use, a complete, constraint system independent, abstract analysis is presented for approximating definiteness information. The analysis is in fact of quite general applicability. It has been implemented and used in the analysis of CLP(R) and Prolog-III applications. Results from the implementation of this analysis are also presented
Strategic directions in constraint programming
An abstract is not available
Automatic optimization of dynamic scheduling in logic programs
Abstract is not available
On the Robustness and Scalability of Semidefinite Relaxation for Optimal Power Flow Problems
Semidefinite relaxation techniques have shown great promise for nonconvex
optimal power flow problems. However, a number of independent numerical
experiments have led to concerns about scalability and robustness of existing
SDP solvers. To address these concerns, we investigate some numerical aspects
of the problem and compare different state-of-the-art solvers. Our results
demonstrate that semidefinite relaxations of large problem instances with on
the order of 10,000 buses can be solved reliably and to reasonable accuracy
within minutes. Furthermore, the semidefinite relaxation of a test case with
25,000 buses can be solved reliably within half an hour; the largest test case
with 82,000 buses is solved within eight hours. We also compare the lower bound
obtained via semidefinite relaxation to locally optimal solutions obtained with
nonlinear optimization methods and calculate the optimality gap
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