Constraint satisfaction in logic programming
Constraint satisfaction in logic programming
Constraint relaxation may be perfect
Artificial Intelligence
Tabulated resolution for well founded semantics
ILPS '93 Proceedings of the 1993 international symposium on Logic programming
XSB as an efficient deductive database engine
SIGMOD '94 Proceedings of the 1994 ACM SIGMOD international conference on Management of data
ILPS '94 Proceedings of the 1994 International Symposium on Logic programming
Tabled evaluation with delaying for general logic programs
Journal of the ACM (JACM)
Value Constraints in the CLP Scheme
Constraints
OLD Resolution with Tabulation
Proceedings of the Third International Conference on Logic Programming
Symbolic-interval cooperation in constraint programming
Proceedings of the 2001 international symposium on Symbolic and algebraic computation
Solving Nonlinear Equations by Abstraction, Gaussian Elimination, and Interval Methods
FroCoS '02 Proceedings of the 4th International Workshop on Frontiers of Combining Systems
Resolution of nonlinear interval problems using symbolic interval arithmetic
Engineering Applications of Artificial Intelligence
Localization of an underwater robot using interval constraint propagation
CP'06 Proceedings of the 12th international conference on Principles and Practice of Constraint Programming
Brief paper: Set-membership state estimation with fleeting data
Automatica (Journal of IFAC)
| Hi-index | 0.00 |
Intervalconstraints can be used to solve problems in numerical analysis.In this paper we show that one can improve the performance ofsuch an interval constraint program by the declarative use ofconstraints that are redundant in the sense of not needed todefine the problem. The first example shows that computationof an unstable recurrence relation can be improved. The secondexample concerns a solver of nonlinear equations. It shows that,by adding as redundant constraints instances of Taylor‘s theorem,one can obtain convergence that appears to be quadratic.