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# library(tentative_constraints)

Tentative value implementations for some basic constraints [more]

## Predicates

> alldifferent_t(+Cs, +MC)
>   Tentative value implementation of alldifferent constraint
> alldifferent_t(+Xs, +Cs, +MC)
>   Tentative value implementation of alldifferent/2 constraint
> eq_t(?X, ?Y, +MC)
>   Tentative value implementation of $=/2 arithmetic equality constraint
> integral_t(?X, ?Epsilon, +MC)
>   Tentative value implementation of approximate integrality
> neq_t(?X, ?Y, +MC)
>   Tentative value implementation of $\=/2 arithmetic disequality constraint

## Reexports

> reexport tentative

## Other Exports

> export op(700, xfx, $=)
> export op(700, xfx, $\=)

## Description

This library contains tentative value implementations for some basic constraints. It is intended to be used together with lib(tentative), which provides the underlying primitives and facilities to create tentative variables, manage constraint sets, and to do search.

## Examples

        %
        % The following code implements a solution to the N-queens problem,
        % using a steepest-ascent hill-climbing heuristic.
        %

        :- lib(tentative).
        :- lib(tentative_constraints).

        queens(N, Board) :-
    	    dim(Board, [N]),			% make variables
    	    tent_set_random(Board, 1..N),	% init tentative values

    	    dim(Pos, [N]),			% aux arrays of constants
    	    ( foreacharg(I,Pos), for(I,0,N-1) do true ),
    	    dim(Neg, [N]),
    	    ( foreacharg(I,Neg), for(I,0,-N+1,-1) do true ),

    	    CS :~ alldifferent(Board),		% setup constraints ...
    	    CS :~ alldifferent(Board, Pos),	% ... in conflict set CS
    	    CS :~ alldifferent(Board, Neg),

    	    cs_violations(CS, TotalViolation),	% search part
    	    steepest(Board, N, TotalViolation),

    	    tent_fix(Board),			% instantiate variables
    	    cs_clear_satisfied(CS).		% clean up conflict set

        steepest(Board, N, Violations) :-
    	    vs_create(Board, Vars),		% create variable set
    	    Violations tent_get V0,		% initial violations
    	    SampleSize is fix(sqrt(N)),		% neighbourhood size
    	    (
    		fromto(V0,_V1,V2,0),		% until no violations left
    		param(Vars,N,SampleSize,Violations)
    	    do
    		vs_random_worst(Vars, X),	% get a most violated variable
    		tent_minimize_random(		% find a best neighbour
    		    (				% nondeterministic move
    			random_sample(1..N,SampleSize,I),
    			X tent_set I
    		    ),
    		    Violations,			% violation variable
    		    I				% best move-id
    		),
    		X tent_set I,			% do the move
    		Violations tent_get V2		% new violations
    	    ).

## About

- Author: Joachim Schimpf
- Copyright © Cisco Systems
- Date: $Date: 2009/07/16 09:11:27 $

## See Also

library(tentative)

Generated from tentative_constraints.eci on 2020-02-26 22:26