TPTP Problem File: NUM862_1.p
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- Solve Problem
%------------------------------------------------------------------------------
% File : NUM862_1 : TPTP v9.0.0. Released v4.1.0.
% Domain : Number Theory
% Problem : Upper bound replace maximum embedded in a context (1)+(2)
% Version : Especial.
% English : This is an abstraction of a problem to show equivalence of two
% given constraint models. More precisely, the task is to prove that
% the minimal solutions of a certain constraint model are preserved
% if the applications of the "maximum" function in it are replaced
% by "upper bounds" only.
% Refs : [Bau10] Baumgartner (2010), Email to G. Sutcliffe
% : [BS09] Baumgartner & Slaney (2009), Constraint Modelling: A C
% Source : [Bau10]
% Names :
% Status : Theorem
% Rating : 0.38 v9.0.0, 0.25 v8.2.0, 0.38 v7.5.0, 0.50 v7.4.0, 0.38 v7.3.0, 0.33 v7.0.0, 0.43 v6.4.0, 0.67 v6.3.0, 0.71 v6.2.0, 0.75 v6.1.0, 0.78 v6.0.0, 0.86 v5.5.0, 0.89 v5.4.0, 1.00 v5.3.0, 0.90 v5.2.0, 1.00 v5.0.0
% Syntax : Number of formulae : 17 ( 0 unt; 8 typ; 0 def)
% Number of atoms : 24 ( 2 equ)
% Maximal formula atoms : 4 ( 1 avg)
% Number of connectives : 17 ( 2 ~; 2 |; 4 &)
% ( 7 <=>; 2 =>; 0 <=; 0 <~>)
% Maximal formula depth : 8 ( 6 avg)
% Maximal term depth : 4 ( 1 avg)
% Number arithmetic : 39 ( 10 atm; 2 fun; 0 num; 27 var)
% Number of types : 2 ( 0 usr; 1 ari)
% Number of type conns : 18 ( 7 >; 11 *; 0 +; 0 <<)
% Number of predicates : 7 ( 5 usr; 0 prp; 2-3 aty)
% Number of functors : 4 ( 3 usr; 1 con; 0-2 aty)
% Number of variables : 27 ( 26 !; 1 ?; 27 :)
% SPC : TF0_THM_EQU_ARI
% Comments :
%------------------------------------------------------------------------------
tff(c_type,type,
c: $int ).
tff(summation_type,type,
summation: $int > $int ).
tff(ub_type,type,
ub: ( $int * $int * $int ) > $o ).
tff(model_max_type,type,
model_max: ( $int * $int * $int ) > $o ).
tff(model_ub_type,type,
model_ub: ( $int * $int * $int ) > $o ).
tff(minsol_model_max_type,type,
minsol_model_max: ( $int * $int * $int ) > $o ).
tff(minsol_model_ub_type,type,
minsol_model_ub: ( $int * $int * $int ) > $o ).
tff(max_type,type,
max: ( $int * $int ) > $int ).
%----summation(X) is meant as an abstraction of a certain summation term in
%----the original constraint problem
tff(summation_monotone,axiom,
! [X: $int,Y: $int] :
( $lesseq(X,Y)
<=> $lesseq(summation(X),summation(Y)) ) ).
%----Maximum function
tff(max_1,axiom,
! [X: $int,Y: $int] :
( ( max(X,Y) = X )
| ~ $lesseq(Y,X) ) ).
tff(max_2,axiom,
! [X: $int,Y: $int] :
( ( max(X,Y) = Y )
| ~ $lesseq(X,Y) ) ).
%----Z is an upper bound of Y and Z
tff(ub,axiom,
! [X: $int,Y: $int,Z: $int] :
( ub(X,Y,Z)
<=> ( $lesseq(X,Z)
& $lesseq(Y,Z) ) ) ).
%----The model - version with max
tff(model_max_5,axiom,
! [X: $int,Y: $int,N: $int] :
( model_max(X,Y,N)
<=> $lesseq($sum(c,summation(max(X,Y))),N) ) ).
%----The model - version with ub
tff(model_ub_5,axiom,
! [X: $int,Y: $int,N: $int] :
( model_ub(X,Y,N)
<=> ? [Z: $int] :
( ub(X,Y,Z)
& $lesseq($sum(c,summation(Z)),N) ) ) ).
%----minimal solution, model_max
tff(minsol_model_max,axiom,
! [X: $int,Y: $int,N: $int] :
( minsol_model_max(X,Y,N)
<=> ( model_max(X,Y,N)
& ! [Z: $int] :
( model_max(X,Y,Z)
=> $lesseq(N,Z) ) ) ) ).
%----minimal solution, model_ub
tff(minsol_model_ub,axiom,
! [X: $int,Y: $int,N: $int] :
( minsol_model_ub(X,Y,N)
<=> ( model_ub(X,Y,N)
& ! [Z: $int] :
( model_ub(X,Y,Z)
=> $lesseq(N,Z) ) ) ) ).
%----Conjecture: minimal solutions of model_max and model_ub are the same:
tff(max_is_ub_1,conjecture,
! [X: $int,Y: $int,Z: $int] :
( minsol_model_ub(X,Y,Z)
<=> minsol_model_max(X,Y,Z) ) ).
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