Faster implementation of verbal arithmetic in Prolog - performance
I already made a working generalized verbal arithmetic solver in Prolog but it's too slow. It takes 8 minutes just to run the simple expression S E N D + M O R E = M O N E Y. Can someone help me make it run faster?
/* verbalArithmetic(List,Word1,Word2,Word3) where List is the list of all
possible letters in the words. The SEND+MORE = MONEY expression would then
be represented as
verbalArithmetic([S,E,N,D,M,O,R,Y],[S,E,N,D],[M,O,R,E],[M,O,N,E,Y]). */
validDigit(X) :- member(X,[0,1,2,3,4,5,6,7,8,9]).
validStart(X) :- member(X,[1,2,3,4,5,6,7,8,9]).
assign([H|[]]) :- validDigit(H).
assign([H|Tail]) :- validDigit(H), assign(Tail), fd_all_different([H|Tail]).
findTail(List,H,T) :- append(H,[T],List).
convert([T],T) :- validDigit(T).
convert(List,Num) :- findTail(List,H,T), convert(H,HDigit), Num is (HDigit*10+T).
verbalArithmetic(WordList,[H1|Tail1],[H2|Tail2],Word3) :-
validStart(H1), validStart(H2), assign(WordList),
convert([H1|Tail1],Num1),convert([H2|Tail2],Num2), convert(Word3,Num3),
Sum is Num1+Num2, Num3 = Sum.
Consider using finite domain constraints, for example, in SWI-Prolog:
:- use_module(library(clpfd)).
puzzle([S,E,N,D] + [M,O,R,E] = [M,O,N,E,Y]) :-
Vars = [S,E,N,D,M,O,R,Y],
Vars ins 0..9,
all_different(Vars),
S*1000 + E*100 + N*10 + D +
M*1000 + O*100 + R*10 + E #=
M*10000 + O*1000 + N*100 + E*10 + Y,
M #\= 0, S #\= 0.
Example query:
?- time((puzzle(As+Bs=Cs), label(As))).
% 5,803 inferences, 0.002 CPU in 0.002 seconds (98% CPU, 3553582 Lips)
As = [9, 5, 6, 7],
Bs = [1, 0, 8, 5],
Cs = [1, 0, 6, 5, 2] ;
% 1,411 inferences, 0.001 CPU in 0.001 seconds (97% CPU, 2093472 Lips)
false.
Poor performance here is due to forming all possible letter assignments before checking if any are feasible.
My advice is "fail early, fail often". That is, push as many checks for failure as early as possible into the assignment steps, thus pruning the search tree.
Klas Lindbäck makes some good suggestions. As a generalization, when adding two numbers the carry is at most one in each place. So the assignment of distinct digits to letters from left to right can be checked with allowance for the possibility of an as-yet-undetermined carry in the rightmost places. (Of course in the final "units" place, there is no carry.)
It's a lot to think about, which is why constraint logic, as mat suggests (and which you've already broached with fd_all_different/1), is such a convenience.
Added: Here's a Prolog solution without constraint logic, using just one auxiliary predicate omit/3:
omit(H,[H|T],T).
omit(X,[H|T],[H|Y]) :- omit(X,T,Y).
which both selects an item from a list and produces the shortened list without that item.
Here then is the code for sendMoreMoney/3 that searches by evaluating the sum from left to right:
sendMoreMoney([S,E,N,D],[M,O,R,E],[M,O,N,E,Y]) :-
M = 1,
omit(S,[2,3,4,5,6,7,8,9],PoolO),
(CarryS = 0 ; CarryS = 1),
%% CarryS + S + M = M*10 + O
O is (CarryS + S + M) - (M*10),
omit(O,[0|PoolO],PoolE),
omit(E,PoolE,PoolN),
(CarryE = 0 ; CarryE = 1),
%% CarryE + E + O = CarryS*10 + N
N is (CarryE + E + O) - (CarryS*10),
omit(N,PoolN,PoolR),
(CarryN = 0 ; CarryN = 1),
%% CarryN + N + R = CarryE*10 + E
R is (CarryE*10 + E) - (CarryN + N),
omit(R,PoolR,PoolD),
omit(D,PoolD,PoolY),
%% D + E = CarryN*10 + Y
Y is (D + E) - (CarryN*10),
omit(Y,PoolY,_).
We get off to a quick start by observing that M must be the nonzero carry from the leftmost digits sum, hence 1, and that S must be some other nonzero digit. The comments show steps where additional letters may be deterministically assigned values based on choices already made.
Added(2): Here is a "general" cryptarithm solver for two summands, which need not have the same length/number of "places". Code for length/2 is omitted as a fairly common built-in predicate, and taking up the suggestion by Will Ness, calls to omit/3 are replaced by select/3 for convenience of SWI-Prolog users.
I've tested this with Amzi! and SWI-Prolog using those alphametics examples from Cryptarithms.com which involve two summands, each of which has a unique solution. I also made up an example with a dozen solutions, I + AM = BEN, to test proper backtracking.
solveCryptarithm([H1|T1],[H2|T2],Sum) :-
operandAlign([H1|T1],[H2|T2],Sum,AddTop,AddPad,Carry,TSum,Pool),
solveCryptarithmAux(H1,H2,AddTop,AddPad,Carry,TSum,Pool).
operandAlign(Add1,Add2,Sum,AddTop,AddPad,Carry,TSum,Pool) :-
operandSwapPad(Add1,Add2,Length,AddTop,AddPad),
length(Sum,Size),
( Size = Length
-> ( Carry = 0, Sum = TSum , Pool = [1|Peel] )
; ( Size is Length+1, Carry = 1, Sum = [Carry|TSum], Pool = Peel )
),
Peel = [2,3,4,5,6,7,8,9,0].
operandSwapPad(List1,List2,Length,Longer,Padded) :-
length(List1,Length1),
length(List2,Length2),
( Length1 >= Length2
-> ( Length = Length1, Longer = List1, Shorter = List2, Pad is Length1 - Length2 )
; ( Length = Length2, Longer = List2, Shorter = List1, Pad is Length2 - Length1 )
),
zeroPad(Shorter,Pad,Padded).
zeroPad(L,0,L).
zeroPad(L,K,P) :-
K > 0,
M is K-1,
zeroPad([0|L],M,P).
solveCryptarithmAux(_,_,[],[],0,[],_).
solveCryptarithmAux(NZ1,NZ2,[H1|T1],[H2|T2],CarryOut,[H3|T3],Pool) :-
( CarryIn = 0 ; CarryIn = 1 ), /* anticipatory carry */
( var(H1)
-> select(H1,Pool,P_ol)
; Pool = P_ol
),
( var(H2)
-> select(H2,P_ol,P__l)
; P_ol = P__l
),
( var(H3)
-> ( H3 is H1 + H2 + CarryIn - 10*CarryOut, select(H3,P__l,P___) )
; ( H3 is H1 + H2 + CarryIn - 10*CarryOut, P__l = P___ )
),
NZ1 \== 0,
NZ2 \== 0,
solveCryptarithmAux(NZ1,NZ2,T1,T2,CarryIn,T3,P___).
I think this illustrates that the advantages of left-to-right search/evaluation can be attained in a "generalized" solver, increasing the number of inferences by roughly a factor of two in comparison with the earlier "tailored" code.
Note: This answer discusses an algorithm for reducing the number of combinations that need to be tried. I don't know Prolog, so I can't provide any code snippets.
The trick to speed up a brute force solution is shortcuts. If you can identify a range of combinations that are invalid you can reduce the number of combinations substantially.
Take the example in hand. When a human solves it, she immediately notices that MONEY has 5 digits while SEND and MORE only have 4, so the M in MONEY must be the digit 1. 90% of the combinations gone!
When constructing an algorithm for a computer, we try to use shortcuts that apply to all possible input first. If they fail to give the required performance we start looking at shortcuts that only apply to specific combinations of input.
So we leave the M=1 shortcut for now.
Instead, I would focus on the last digits.
We know that (D+E) mod 10 = Y.
That's our 90% reduction in the number of combinations to try.
That step should bring exacution to just under a minute.
What can we do if that's not enough?
Next step:
Look at the second to last digit!
We know that (N+R+carry from D+E) mod 10 = E.
Since we are testing through all valid combinations of the last digit, for each test we will know whether the carry is 0 or 1.
A complication (for the code) that further reduces the number of combinations to be tested is that we will encounter duplicates (a letter gets mapped to a number that is already assigned to another letter). When we encounter a duplicate, we can advance to the next combination without going further down the chain.
Good luck with your assignment!
Here's my take on it. I use clpfd, dcg,
and meta-predicate mapfoldl/5:
:- meta_predicate mapfoldl(4,?,?,?,?).
mapfoldl(P_4,Xs,Zs, S0,S) :-
list_mapfoldl_(Xs,Zs, S0,S, P_4).
:- meta_predicate list_mapfoldl_(?,?,?,?,4).
list_mapfoldl_([],[], S,S, _).
list_mapfoldl_([X|Xs],[Y|Ys], S0,S, P_4) :-
call(P_4,X,Y,S0,S1),
list_mapfoldl_(Xs,Ys, S1,S, P_4).
Let's put mapfoldl/5 to good use and do some verbal arithmetic!
:- use_module(library(clpfd)).
:- use_module(library(lambda)).
digits_number(Ds,Z) :-
Ds = [D0|_],
Ds ins 0..9,
D0 #\= 0, % most-significant digit must not equal 0
reverse(Ds,Rs),
length(Ds,N),
numlist(1,N,Es), % exponents (+1)
maplist(\E1^V^(V is 10**(E1-1)),Es,Ps),
scalar_product(Ps,Rs,#=,Z).
list([]) --> [].
list([E|Es]) --> [E], list(Es).
cryptarithexpr_value([V|Vs],X) -->
{ digits_number([V|Vs],X) },
list([V|Vs]).
cryptarithexpr_value(T0,T) -->
{ functor(T0,F,A) },
{ dif(F-A,'.'-2) },
{ T0 =.. [F|Args0] },
mapfoldl(cryptarithexpr_value,Args0,Args),
{ T =.. [F|Args] }.
crypt_arith_(Expr,Zs) :-
phrase(cryptarithexpr_value(Expr,Goal),Zs0),
( member(Z,Zs0), \+var(Z)
-> throw(error(uninstantiation_error(Expr),crypt_arith_/2))
; true
),
sort(Zs0,Zs),
all_different(Zs),
call(Goal).
Quick and dirty hack to dump all solutions found:
solve_n_dump(Opts,Eq) :-
( crypt_arith_(Eq,Zs),
labeling(Opts,Zs),
format('Eq = (~q), Zs = ~q.~n',[Eq,Zs]),
false
; true
).
solve_n_dump(Eq) :- solve_n_dump([],Eq).
Let's try it!
?- solve_n_dump([S,E,N,D]+[M,O,R,E] #= [M,O,N,E,Y]).
Eq = ([9,5,6,7]+[1,0,8,5]#=[1,0,6,5,2]), Zs = [9,5,6,7,1,0,8,2].
true.
?- solve_n_dump([C,R,O,S,S]+[R,O,A,D,S] #= [D,A,N,G,E,R]).
Eq = ([9,6,2,3,3]+[6,2,5,1,3]#=[1,5,8,7,4,6]), Zs = [9,6,2,3,5,1,8,7,4].
true.
?- solve_n_dump([F,O,R,T,Y]+[T,E,N]+[T,E,N] #= [S,I,X,T,Y]).
Eq = ([2,9,7,8,6]+[8,5,0]+[8,5,0]#=[3,1,4,8,6]), Zs = [2,9,7,8,6,5,0,3,1,4].
true.
?- solve_n_dump([E,A,U]*[E,A,U] #= [O,C,E,A,N]).
Eq = ([2,0,3]*[2,0,3]#=[4,1,2,0,9]), Zs = [2,0,3,4,1,9].
true.
?- solve_n_dump([N,U,M,B,E,R] #= 3*[P,R,I,M,E]).
% same as: [N,U,M,B,E,R] #= [P,R,I,M,E]+[P,R,I,M,E]+[P,R,I,M,E]
Eq = (3*[5,4,3,2,8]#=[1,6,2,9,8,4]), Zs = [5,4,3,2,8,1,6,9].
true.
?- solve_n_dump(3*[C,O,F,F,E,E] #= [T,H,E,O,R,E,M]).
Eq = (3*[8,3,1,1,9,9]#=[2,4,9,3,5,9,7]), Zs = [8,3,1,9,2,4,5,7].
true.
Let's do some more and try some different labeling options:
?- time(solve_n_dump([],[D,O,N,A,L,D]+[G,E,R,A,L,D] #= [R,O,B,E,R,T])).
Eq = ([5,2,6,4,8,5]+[1,9,7,4,8,5]#=[7,2,3,9,7,0]), Zs = [5,2,6,4,8,1,9,7,3,0].
% 35,696,801 inferences, 3.929 CPU in 3.928 seconds (100% CPU, 9085480 Lips)
true.
?- time(solve_n_dump([ff],[D,O,N,A,L,D]+[G,E,R,A,L,D] #= [R,O,B,E,R,T])).
Eq = ([5,2,6,4,8,5]+[1,9,7,4,8,5]#=[7,2,3,9,7,0]), Zs = [5,2,6,4,8,1,9,7,3,0].
% 2,902,871 inferences, 0.340 CPU in 0.340 seconds (100% CPU, 8533271 Lips)
true.
Will Ness style, generalized (but assuming length(A) <= length(B)) solver:
money_puzzle(A, B, C) :-
maplist(reverse, [A,B,C], [X,Y,Z]),
numlist(0, 9, Dom),
swc(0, Dom, X,Y,Z),
A \= [0|_], B \= [0|_].
swc(C, D0, [X|Xs], [Y|Ys], [Z|Zs]) :-
peek(D0, X, D1),
peek(D1, Y, D2),
peek(D2, Z, D3),
S is X+Y+C,
( S > 9 -> Z is S - 10, C1 = 1 ; Z = S, C1 = 0 ),
swc(C1, D3, Xs, Ys, Zs).
swc(C, D0, [], [Y|Ys], [Z|Zs]) :-
peek(D0, Y, D1),
peek(D1, Z, D2),
S is Y+C,
( S > 9 -> Z is S - 10, C1 = 1 ; Z = S, C1 = 0 ),
swc(C1, D2, [], Ys, Zs).
swc(0, _, [], [], []).
swc(1, _, [], [], [1]).
peek(D, V, R) :- var(V) -> select(V, D, R) ; R = D.
performance:
?- time(money_puzzle([S,E,N,D],[M,O,R,E],[M,O,N,E,Y])).
% 38,710 inferences, 0.016 CPU in 0.016 seconds (100% CPU, 2356481 Lips)
S = 9,
E = 5,
N = 6,
D = 7,
M = 1,
O = 0,
R = 8,
Y = 2 ;
% 15,287 inferences, 0.009 CPU in 0.009 seconds (99% CPU, 1685686 Lips)
false.
?- time(money_puzzle([D,O,N,A,L,D],[G,E,R,A,L,D],[R,O,B,E,R,T])).
% 14,526 inferences, 0.008 CPU in 0.008 seconds (99% CPU, 1870213 Lips)
D = 5,
O = 2,
N = 6,
A = 4,
L = 8,
G = 1,
E = 9,
R = 7,
B = 3,
T = 0 ;
% 13,788 inferences, 0.009 CPU in 0.009 seconds (99% CPU, 1486159 Lips)
false.
You have
convert([A,B,C,D]) => convert([A,B,C])*10 + D
=> (convert([A,B])*10+C)*10+D => ...
=> ((A*10+B)*10+C)*10+D
So, you can express this with a simple linear recursion.
More importantly, when you pick one possible digit from your domain 0..9, you shouldn't use that digit anymore for subsequent choices:
selectM([A|As],S,Z):- select(A,S,S1),selectM(As,S1,Z).
selectM([],Z,Z).
select/3 is available in SWI Prolog. Armed with this tool, you can select your digits gradually from your thus narrowing domain:
money_puzzle( [[S,E,N,D],[M,O,R,E],[M,O,N,E,Y]]):-
Dom = [0,1,2,3,4,5,6,7,8,9],
selectM([D,E], Dom,Dom1), add(D,E,0, Y,C1), % D+E=Y
selectM([Y,N,R],Dom1,Dom2), add(N,R,C1,E,C2), % N+R=E
select( O, Dom2,Dom3), add(E,O,C2,N,C3), % E+O=N
selectM([S,M], Dom3,_), add(S,M,C3,O,M), % S+M=MO
S \== 0, M \== 0.
We can add two digits with a carry, add produce a resulting digit with new carry (say, 4+8 (0) = 2 (1) i.e. 12):
add(A,B,C1,D,C2):- N is A+B+C1, D is N mod 10, C2 is N // 10 .
Thus implemented, money_puzzle/1 runs instantaneously, thanks to the gradual nature in which the digits are picked and tested right away:
?- time( money_puzzle(X) ).
% 27,653 inferences, 0.02 CPU in 0.02 seconds (100% CPU, 1380662 Lips)
X = [[9, 5, 6, 7], [1, 0, 8, 5], [1, 0, 6, 5, 2]] ;
No
?- time( (money_puzzle(X),fail) ).
% 38,601 inferences, 0.02 CPU in 0.02 seconds (100% CPU, 1927275 Lips)
The challenge becomes now to make it generic.
Related
Incrementing value on backtrack
how can I do increment on backtracking ... so that goal(S) receives incremented number .. every time it fails on the next run I want to get the next number S1 is S + 1,goal(S1) does not work, because : ?- S=0, S1 is S+1. S = 0, S1 = 1. ?- S=0,between(1,3,_), S1 is S+1. S = 0, S1 = 1 ; S = 0, S1 = 1 ; S = 0, S1 = 1. this work %%counting baz(..,C) :- .... arg(...), Y is X + 1, nb_setarg(...), goal(Y), ... foo(..C) :- ....baz(....,C)..., foo(...C). %%counter blah :- ....foo(....,counter(0))... this is not working, i think cause the recursive foo() would force baz() to initialize counter(0)... but i'm good with #sligo solution above baz(..) :- C = counter(0), .... arg(...), Y is X + 1, nb_setarg(...), goal(Y), ... foo(..) :- ....baz(....)..., foo(...).
so that goal(S) receives incremented number .. every time it fails on the next run I want to get the next number That's what between/3 does? Every time on backtracking it makes the next number: goal(X) :- write('inside goal, X is '), write(X), nl. test :- between(0, 3, S), goal(S). e.g. ?- test. inside goal, X is 0 true ; inside goal, X is 1 true ; inside goal, X is 2 true ; inside goal, X is 3 true ; Edit: From the help for between/3: between(+Low, +High, ?Value) Low and High are integers, High >=Low. If Value is an integer, Low =<Value =<High. When Value is a variable it is successively bound to all integers between Low and High. If High is inf or infinite between/3 is true iff Value >=Low, a feature that is particularly interesting for generating integers from a certain value. (And see the comments on the help page by LogicalCaptain)
Use non-backtrackable destructive assignment predicate nb_setarg/3: ?- C = counter(0), between(1, 3, _), arg(1, C, X), Y is X + 1, nb_setarg(1, C, Y). C = counter(1), X = 0, Y = 1 ; C = counter(2), X = 1, Y = 2 ; C = counter(3), X = 2, Y = 3. Alternatives: foo(C) :- between(1, inf, C), goal(C), !. baz(C) :- C = counter(0), repeat, arg(1, C, X), Y is X + 1, nb_setarg(1, C, Y), goal(Y), !. goal(X) :- X > 9. Examples: ?- foo(C). C = 10. ?- baz(C). C = counter(10).
Arithmetics in Prolog, represent a number using powers of 2
I have two numbers, let's name them N and K, and I want to write N using K powers of 2. For example if N = 9 and K = 4, then N could be N = 1 + 2 + 2 + 4 (2^0 + 2^1 + 2^1 + 2^2). My program should output something like N = [1,2,2,4]. I am used to C++. I can't find a way to solve this problem in Prolog. Any help will be appreciated!
I thought this would be a few-liner using CLP(FD), but no dice. Can it be done simpler? So here is the complete solution. Don't think I came up with this in one attempt, there are a few iterations and dead ends in there. :- use_module(library(debug)). % --- % powersum(+N,+Target,?Solution) % --- % Entry point. Relate a list "Solution" of "N" integers to the integer % "Target", which is the sum of 2^Solution[i]. % This works only in the "functional" direction % "Compute Solution as powersum(N,Target)" % or the "verification" direction % "is Solution a solution of powersum(N,Target)"? % % An extension of some interest would be to NOT have a fixed "N". % Let powersum/2 find appropriate N. % % The search is subject to exponential slowdown as the list length % increases, so one gets bogged down quickly. % --- powersum(N,Target,Solution) :- ((integer(N),N>0,integer(Target),Target>=1) -> true ; throw("Bad args!")), length(RS,N), % create a list RN of N fresh variables MaxPower is floor(log(Target)/log(2)), % that's the largest power we will find in the solution propose(RS,MaxPower,Target,0), % generate & test a solution into RS reverse(RS,Solution), % if we are here, we found something! Reverse RS so that it is increasing my_write(Solution,String,Value), % prettyprinting format("~s = ~d\n",[String,Value]). % --- % propose(ListForSolution,MaxPowerHere,Target,SumSoFar) % --- % This is an integrate "generate-and-test". It is integrated % to "fail fast" during proposal - we don't want to propose a % complete solution, then compute the value for that solution % and find out that we overshot the target. If we overshoot, we % want to find ozut immediately! % % So: Propose a new value for the leftmost position L of the % solution list. We are allowed to propose any integer for L % from the sequence [MaxPowerHere,...,0]. "Target" is the target % value we must not overshoot (indeed, we which must meet % exactly at the end of recursion). "SumSoFar" is the sum of % powers "to our left" in the solution list, to which we already % committed. propose([L|Ls],MaxPowerHere,Target,SumSoFar) :- assertion(SumSoFar=<Target), (SumSoFar=Target -> false ; true), % a slight optimization, no solution if we already reached Target! propose_value(L,MaxPowerHere), % Generate: L is now (backtrackably) some value from [MaxPowerHere,...,0] NewSum is (SumSoFar + 2**L), NewSum =< Target, % Test; if this fails, we backtrack to propose_value/2 and will be back with a next L NewMaxPowerHere = L, % Test passed; the next power in the sequence should be no larger than the current, i.e. L propose(Ls,NewMaxPowerHere,Target,NewSum). % Recurse over rest-of-list. propose([],_,Target,Target). % Terminal test: Only succeed if all values set and the Sum is the Target! % --- % propose_value(?X,+Max). % --- % Give me a new value X between [Max,0]. % Backtracks over monotonically decreasing integers. % See the test code for examples. % % One could also construct a list of integers [Max,...,0], then % use "member/2" for backtracking. This would "concretize" the predicate's % behaviour with an explicit list structure. % % "between/3" sadly only generates increasing sequences otherwise one % could use that. Maybe there is a "between/4" taking a step value somewhere? % --- propose_value(X,Max) :- assertion((integer(Max),Max>=0)), Max=X. propose_value(X,Max) :- assertion((integer(Max),Max>=0)), Max>0, succ(NewMax,Max), propose_value(X,NewMax). % --- % I like some nice output, so generate a string representing the solution. % Also, recompute the value to make doubly sure! % --- my_write([L|Ls],String,Value) :- my_write(Ls,StringOnTheRight,ValueOnTheRight), Value is ValueOnTheRight + 2**L, with_output_to(string(String),format("2^~d + ~s",[L,StringOnTheRight])). my_write([L],String,Value) :- with_output_to(string(String),format("2^~d",[L])), Value is 2**L. :- begin_tests(powersum). % powersum(N,Target,Solution) test(pv1) :- bagof(X,propose_value(X,3),Bag), Bag = [3,2,1,0]. test(pv2) :- bagof(X,propose_value(X,2),Bag), Bag = [2,1,0]. test(pv2) :- bagof(X,propose_value(X,1),Bag), Bag = [1,0]. test(pv3) :- bagof(X,propose_value(X,0),Bag), Bag = [0]. test(one) :- bagof(S,powersum(1,1,S),Bag), Bag = [[0]]. test(two) :- bagof(S,powersum(3,10,S),Bag), Bag = [[0,0,3],[1,2,2]]. test(three) :- bagof(S,powersum(3,145,S),Bag), Bag = [[0,4,7]]. test(four,fail) :- powersum(3,8457894,_). test(five) :- bagof(S,powersum(9,8457894,S), Bag), Bag = [[1, 2, 5, 7, 9, 10, 11, 16, 23]]. %% VERY SLOW :- end_tests(powersum). rt :- run_tests(powersum). Running test of 2 minutes due to the last unit testing line... ?- time(rt). % PL-Unit: powersum ....2^0 = 1 .2^0 + 2^0 + 2^3 = 10 2^1 + 2^2 + 2^2 = 10 .2^0 + 2^4 + 2^7 = 145 ..2^1 + 2^2 + 2^5 + 2^7 + 2^9 + 2^10 + 2^11 + 2^16 + 2^23 = 8457894 . done % All 9 tests passed % 455,205,628 inferences, 114.614 CPU in 115.470 seconds (99% CPU, 3971641 Lips) true.
EDIT: With some suggestive comments from repeat, here is a complete, efficient CLP(FD) solution: powersum2_(N, Target, Exponents, Solution) :- length(Exponents, N), MaxExponent is floor(log(Target) / log(2)), Exponents ins 0..MaxExponent, chain(Exponents, #>=), maplist(exponent_power, Exponents, Solution), sum(Solution, #=, Target). exponent_power(Exponent, Power) :- Power #= 2^Exponent. powersum2(N, Target, Solution) :- powersum2_(N, Target, Exponents, Solution), labeling([], Exponents). Ordering exponents by #>= cuts down the search space by excluding redundant permutations. But it is also relevant for the order of labeling (with the [] strategy). The core relation powersum2_/4 posts constraints on the numbers: ?- powersum2_(5, 31, Exponents, Solution). Exponents = [_954, _960, _966, _972, _978], Solution = [_984, _990, _996, _1002, _1008], _954 in 0..4, _954#>=_960, 2^_954#=_984, _960 in 0..4, _960#>=_966, 2^_960#=_990, _966 in 0..4, _966#>=_972, 2^_966#=_996, _972 in 0..4, _972#>=_978, 2^_972#=_1002, _978 in 0..4, 2^_978#=_1008, _1008 in 1..16, _984+_990+_996+_1002+_1008#=31, _984 in 1..16, _990 in 1..16, _996 in 1..16, _1002 in 1..16. And then labeling searches for the actual solutions: ?- powersum2(5, 31, Solution). Solution = [16, 8, 4, 2, 1] ; false. This solution is considerably more efficient than the other answers so far: ?- time(powersum2(9, 8457894, Solution)). % 6,957,285 inferences, 0.589 CPU in 0.603 seconds (98% CPU, 11812656 Lips) Solution = [8388608, 65536, 2048, 1024, 512, 128, 32, 4, 2]. Original version follows. Here is another CLP(FD) solution. The idea is to express "power of two" as a "real" constraint, i.e, not as a predicate that enumerates numbers like lurker's power_of_2/1 does. It helps that the actual constraint to be expressed isn't really "power of two", but rather "power of two less than or equal to a known bound". So here is some clumsy code to compute a list of powers of two up to a limit: powers_of_two_bound(PowersOfTwo, UpperBound) :- powers_of_two_bound(1, PowersOfTwo, UpperBound). powers_of_two_bound(Power, [Power], UpperBound) :- Power =< UpperBound, Power * 2 > UpperBound. powers_of_two_bound(Power, [Power | PowersOfTwo], UpperBound) :- Power =< UpperBound, NextPower is Power * 2, powers_of_two_bound(NextPower, PowersOfTwo, UpperBound). ?- powers_of_two_bound(Powers, 1023). Powers = [1, 2, 4, 8, 16, 32, 64, 128, 256|...] ; false. ... and then to compute a constraint term based on this... power_of_two_constraint(UpperBound, Variable, Constraint) :- powers_of_two_bound(PowersOfTwo, UpperBound), maplist(fd_equals(Variable), PowersOfTwo, PowerOfTwoConstraints), constraints_operator_combined(PowerOfTwoConstraints, #\/, Constraint). fd_equals(Variable, Value, Variable #= Value). constraints_operator_combined([Constraint], _Operator, Constraint). constraints_operator_combined([C | Cs], Operator, Constraint) :- Constraint =.. [Operator, C, NextConstraint], constraints_operator_combined(Cs, Operator, NextConstraint). ?- power_of_two_constraint(1023, X, Constraint). Constraint = (X#=1#\/(X#=2#\/(X#=4#\/(X#=8#\/(X#=16#\/(X#=32#\/(X#=64#\/(X#=128#\/(... #= ... #\/ ... #= ...))))))))) ; false. ... and then to post that constraint: power_of_two(Target, Variable) :- power_of_two_constraint(Target, Variable, Constraint), call(Constraint). ?- power_of_two(1023, X). X in ... .. ... \/ 4\/8\/16\/32\/64\/128\/256\/512 ; false. (Seeing this printed in this syntax shows me that I could simplify the code computing the constraint term...) And then the core relation is: powersum_(N, Target, Solution) :- length(Solution, N), maplist(power_of_two(Target), Solution), list_monotonic(Solution, #=<), sum(Solution, #=, Target). list_monotonic([], _Operation). list_monotonic([_X], _Operation). list_monotonic([X, Y | Xs], Operation) :- call(Operation, X, Y), list_monotonic([Y | Xs], Operation). We can run this without labeling: ?- powersum_(9, 1023, S). S = [_9158, _9164, _9170, _9176, _9182, _9188, _9194, _9200, _9206], _9158 in ... .. ... \/ 4\/8\/16\/32\/64\/128\/256\/512, _9158+_9164+_9170+_9176+_9182+_9188+_9194+_9200+_9206#=1023, _9164#>=_9158, _9164 in ... .. ... \/ 4\/8\/16\/32\/64\/128\/256\/512, _9170#>=_9164, _9170 in ... .. ... \/ 4\/8\/16\/32\/64\/128\/256\/512, _9176#>=_9170, _9176 in ... .. ... \/ 4\/8\/16\/32\/64\/128\/256\/512, _9182#>=_9176, _9182 in ... .. ... \/ 4\/8\/16\/32\/64\/128\/256\/512, _9188#>=_9182, _9188 in ... .. ... \/ 4\/8\/16\/32\/64\/128\/256\/512, _9194#>=_9188, _9194 in ... .. ... \/ 4\/8\/16\/32\/64\/128\/256\/512, _9200#>=_9194, _9200 in ... .. ... \/ 4\/8\/16\/32\/64\/128\/256\/512, _9206#>=_9200, _9206 in ... .. ... \/ 4\/8\/16\/32\/64\/128\/256\/512 ; false. And it's somewhat quick when we label: ?- time(( powersum_(8, 255, S), labeling([], S) )), format('S = ~w~n', [S]), false. % 561,982 inferences, 0.055 CPU in 0.055 seconds (100% CPU, 10238377 Lips) S = [1,2,4,8,16,32,64,128] % 1,091,295 inferences, 0.080 CPU in 0.081 seconds (100% CPU, 13557999 Lips) false. Contrast this with lurker's approach, which takes much longer even just to find the first solution: ?- time(binary_partition(255, 8, S)), format('S = ~w~n', [S]), false. % 402,226,596 inferences, 33.117 CPU in 33.118 seconds (100% CPU, 12145562 Lips) S = [1,2,4,8,16,32,64,128] % 1,569,157 inferences, 0.130 CPU in 0.130 seconds (100% CPU, 12035050 Lips) S = [1,2,4,8,16,32,64,128] % 14,820,953 inferences, 1.216 CPU in 1.216 seconds (100% CPU, 12190530 Lips) S = [1,2,4,8,16,32,64,128] % 159,089,361 inferences, 13.163 CPU in 13.163 seconds (100% CPU, 12086469 Lips) S = [1,2,4,8,16,32,64,128] % 1,569,155 inferences, 0.134 CPU in 0.134 seconds (100% CPU, 11730834 Lips) S = [1,2,4,8,16,32,64,128] % 56,335,514 inferences, 4.684 CPU in 4.684 seconds (100% CPU, 12027871 Lips) S = [1,2,4,8,16,32,64,128] ^CAction (h for help) ? abort % 1,266,275,462 inferences, 107.019 CPU in 107.839 seconds (99% CPU, 11832284 Lips) % Execution Aborted % got bored of waiting However, this solution is slower than the one by David Tonhofer: ?- time(( powersum_(9, 8457894, S), labeling([], S) )), format('S = ~w~n', [S]), false. % 827,367,193 inferences, 58.396 CPU in 58.398 seconds (100% CPU, 14168325 Lips) S = [2,4,32,128,512,1024,2048,65536,8388608] % 1,715,107,811 inferences, 124.528 CPU in 124.532 seconds (100% CPU, 13772907 Lips) false. versus: ?- time(bagof(S,powersum(9,8457894,S), Bag)). 2^1 + 2^2 + 2^5 + 2^7 + 2^9 + 2^10 + 2^11 + 2^16 + 2^23 = 8457894 % 386,778,067 inferences, 37.705 CPU in 37.706 seconds (100% CPU, 10258003 Lips) Bag = [[1, 2, 5, 7, 9, 10, 11, 16|...]]. There's probably room to improve my constraints, or maybe some magic labeling strategy that will improve the search. EDIT: Ha! Labeling from the largest to the smallest element changes the performance quite dramatically: ?- time(( powersum_(9, 8457894, S), reverse(S, Rev), labeling([], Rev) )), format('S = ~w~n', [S]), false. % 5,320,573 inferences, 0.367 CPU in 0.367 seconds (100% CPU, 14495124 Lips) S = [2,4,32,128,512,1024,2048,65536,8388608] % 67 inferences, 0.000 CPU in 0.000 seconds (100% CPU, 2618313 Lips) false. So this is now about 100x as fast as David Tonhofer's version. I'm content with that :-)
Here's a scheme that uses CLP(FD). In general, when reasoning in the domain of integers in Prolog, CLP(FD) is a good way to go. The idea for this particular problem is to think recursively (as in many Prolog problems) and use a "bifurcation" approach. As David said in his answer, solutions to problems like this don't just flow out on the first attempt. There are preliminary notions, trial implementations, tests, observations, and revisions that go into coming up with the solution to a problem. Even this one could use more work. :) :- use_module(library(clpfd)). % Predicate that succeeds for power of 2 power_of_2(1). power_of_2(N) :- N #> 1, NH #= N // 2, N #= NH * 2, power_of_2(NH). % Predicate that succeeds for a list that is monotonically ascending ascending([_]). ascending([X1,X2|Xs]) :- X1 #=< X2, ascending([X2|Xs]). % Predicate that succeeds if Partition is a K-part partition of N % where the parts are powers of 2 binary_partition(N, K, Partition) :- binary_partition_(N, K, Partition), ascending(Partition). % Only allow ascending lists as solutions binary_partition_(N, 1, [N]) :- % base case power_of_2(N). binary_partition_(N, K, P) :- N #> 1, % constraints on N, K K #> 1, length(P, K), % constraint on P append(LL, LR, P), % conditions on left/right bifurcation NL #> 0, NR #> 0, KL #> 0, KR #> 0, NL #=< NR, % don't count symmetrical cases KL #=< KR, N #= NL + NR, K #= KL + KR, binary_partition_(NL, KL, LL), binary_partition_(NR, KR, LR). This will provide correct results, but it also generates redundant solutions: 2 ?- binary_partition(9,4,L). L = [1, 2, 2, 4] ; L = [1, 2, 2, 4] ; false. As an exercise, you can figure out how to modify it so it only generates unique solutions. :)
my_power_of_two_bound(U,P):- U #>= 2^P, P #=< U, P #>=0. power2(X,Y):- Y #= 2^X. Query: ?- N=9,K=4, length(_List,K), maplist(my_power_of_two_bound(N),_List), maplist(power2,_List,Answer), chain(Answer, #=<), sum(Answer, #=, N), label(Answer). Then: Answer = [1, 2, 2, 4], K = 4, N = 9
How to find the biggest digit in a number in Prolog?
I have an easy task, but somehow I haven't solved it in over an hour. This recursion I am doing isn't working, I'm stuck in an infinte loop. It should compare the last digit of a number with every other and remember the biggest one. Would really like to know why is my logic faulty and how to solve this problem. This is my try on it: maxDigit(X,X):- X<10. maxDigit(X,N):- X1 is X//10, X2 is X mod 10, maxDigit(X1,N1), X2=<N1, N is N1. maxDigit(X,N):- X1 is X//10, X2 is X mod 10, maxDigit(X1,N1), X2>N1, N is X2.
Using SICStus Prolog 4.3.3 we simply combine n_base_digits/3 and maximum/2 like so: ?- n_base_digits(12390238464, 10, _Digits), maximum(Max, _Digits). Max = 9.
A comment suggested stopping as soon as the maximum digit is encountered. This is how we do: :- use_module(library(clpfd)). :- use_module(library(reif)). #=(X, Y, T) :- X #= Y #<==> B, bool10_t(B, T). bool10_t(1, true). bool10_t(0,false). Based on if_/3, (;)/3 and (#=)/3 we then define: n_base_maxdigit(N, Base, D) :- N #> 0, % positive integers only Base #> 1, % smallest base = 2 D #>= 0, D #< Base, n_base_maxdigit0_maxdigit(N, Base, 0, D). n_base_maxdigit0_maxdigit(N, Base, D0, D) :- D1 #= N mod Base, N0 #= N // Base, D2 #= max(D0,D1), if_(( D2 + 1 #= Base ; N0 #= 0 ), D = D2, n_base_maxdigit0_maxdigit(N0, Base, D2, D)). Sample query using SWI-Prolog 7.3.22 with Prolog lambda: ?- use_module(library(lambda)). true. ?- Max+\ ( N is 7^7^7 * 10+9, time(n_base_maxdigit(N,10,Max)) ). % 663 inferences, 0.001 CPU in 0.001 seconds (100% CPU, 1022162 Lips) Max = 9.
You have just to use if/then/else : maxDigit(X,X):- X<10, !. % added after false's remark maxDigit(X,N):- X1 is X//10, X2 is X mod 10, maxDigit(X1,N1), ( X2<N1 -> N = N1 ; N = X2).
in SWI-Prolog could be: maxDigit(N,M) :- number_codes(N,L), max_list(L,T), M is T-0'0.
CLP(FD)-ying Simultaneous Recursion for Fibonacci Lukas Numbers Possible?
There are some instances where recursive predicates can be CLP(FD)-fied with the benefit that the predicate turns bidirectional. What are the limits of this method? For example can the following computation CLP(FD)-fied: Fn: n-th Fibonacci Number Ln: n-th Lucas Number (starting with 2) By this doubling recursion step: F2n = Fn*Ln L2n = (5*Fn^2+Ln^2)//2 And this incrementing recursion step: Fn+1 = (Fn+Ln)//2 Ln+1 = (5*Fn+Ln)//2 The traditional Prolog realization works already from n to Fn. Can this be turned into a CLP(FD) program preserving the fast recursion and at the same time making it bidirectionally, for example figuring out the index n for Fn=377? If yes how? If not why? Bye
Yes, it can be done by constraining the values. You can also move the recursion to be tail recursion, although it's not required to get the solutions: fibluc(0, 0, 2). fibluc(1, 1, 1). fibluc(N, F, L) :- N in 2..1000, % Pick a reasonable value here for 1000 [F, L] ins 1..sup, N rem 2 #= 1, M #= N-1, F #= (F1 + L1) // 2, L #= (5*F1 + L1) // 2, fibluc(M, F1, L1). fibluc(N, F, L) :- N in 2..1000, % Pick a reasonable value here for 1000 [F, L] ins 1..sup, N rem 2 #= 0, M #= N // 2, F #= F1 * L1, L #= (5*F1*F1 + L1*L1) // 2, fibluc(M, F1, L1). Will yield: ?- fibluc(10, X, Y). X = 55, Y = 123 ; false. ?- fibluc(N, 55, Y). N = 10, Y = 123 ; false. ?- fibluc(N, X, 123). N = 10, X = 55 ; false. ?- fibluc(N, 55, 123). N = 10 ; false. ?- fibluc(N, 55, 125). false. ?- fibluc(N, X, Y). N = X, X = 0, Y = 2 ; N = X, X = Y, Y = 1 ; N = 3, X = 2, Y = 4 ; N = 7, X = 13, Y = 29 ; N = 15, X = 610, Y = 1364 ; N = 31, X = 1346269, Y = 3010349 ; N = 63, X = 6557470319842, Y = 14662949395604 ; ... This could be modified to generate results for increasing values of N when N is uninstantiated. Here's a timed, compound query example, run in SWI Prolog 7.1.33 under Linux: ?- time((fibluc(100, X, Y), fibluc(N, X, Z))). % 11,337,988 inferences, 3.092 CPU in 3.100 seconds (100% CPU, 3666357 Lips) X = 354224848179261915075, Y = Z, Z = 792070839848372253127, N = 100 ; % 1,593,620 inferences, 0.466 CPU in 0.468 seconds (100% CPU, 3417800 Lips) false. ?- Using SWI Prolog 7.2.3 with the same code above and the same compound query, the code does go off for a very long time. I waited at least 15 minutes without termination. It's still running right now... I may check on it in the morning. :) I did, however, re-arrange the above code to move the recursive call back to where the original code had it as follows: fibluc(0, 0, 2). fibluc(1, 1, 1). fibluc(N, F, L) :- N in 2..1000, % Pick a reasonable value here for 1000 [F, L] ins 1..sup, N rem 2 #= 1, M #= N-1, fibluc(M, F1, L1), F #= (F1 + L1) // 2, L #= (5*F1 + L1) // 2. fibluc(N, F, L) :- N in 2..1000, % Pick a reasonable value here for 1000 [F, L] ins 1..sup, N rem 2 #= 0, M #= N // 2, fibluc(M, F1, L1), F #= F1 * L1, L #= (5*F1*F1 + L1*L1) // 2. In this case, the favorable results returned: ?- time((fibluc(100, X, Y), fibluc(N, X, Z))). % 10,070,701 inferences, 3.216 CPU in 3.222 seconds (100% CPU, 3131849 Lips) X = 354224848179261915075, Y = Z, Z = 792070839848372253127, N = 100 ; % 1,415,320 inferences, 0.493 CPU in 0.496 seconds (100% CPU, 2868423 Lips) false. Note that the performance of CLP(FD) can be vastly different between different Prolog interpreters. It's interesting that, with SWI Prolog, the ability to handle the tail recursive case was temporarily there with version 7.1.33.
Use reified constraints to make 3 numbers consecutive
Here's an outline of my SWI-Prolog program: :- use_module(library(clpfd)). consec1(L) :- L=[L1,L2,L3,L4,L5,L6,L7,L8,L9], L ins 1..9, ..., abs(L5-L4)#=1, all_different(L), labeling([],L) abs(L5-L4)#=1 makes L5 and L4 next to each other. If I wanted to make three numbers next to each other e.g. L3, L4 and L5, how could I use reified constraints to do this? E.g. L3=4,L5=5,L4=6 or L4=7,L5=8,L3=9
This implements consecutive in the sense you gave in the comments. For a list of N values, we need space enough to make all the values fit in between, and all values need to be different. consecutive([]). % debatable case consecutive(Xs) :- Xs = [_|_], length(Xs, N), all_different(Xs), max_of(Max, Xs), min_of(Min, Xs), Max-Min #= N-1. max_of(Max, [Max]). max_of(Max0, [E|Es]) :- Max0 #= max(E,Max1), max_of(Max1, Es). min_of(Min, [Min]). min_of(Min0, [E|Es]) :- Min0 #= min(E, Min1), min_of(Min1, Es).
TL;DR: too long for a comment: play-time with specialized sicstus-prolog clpfd constraints This answer follows up this previous answer; recent versions of SICStus Prolog offer specialized clpfd constraints maximum/2 and minimum/2 as alternatives to min_of/2 and max_of/2. Using the following code for benchmarking1,2 ... :- use_module(library(clpfd)). :- use_module(library(between)). bench_(How, N, Ub) :- \+ \+ ( length(Xs, N), domain(Xs, 1, Ub), all_different(Xs), Max-Min #= N-1, ( How = 0 ; How = min_of , max_of( Max, Xs), min_of( Min, Xs) ; How = minimum, maximum(Max, Xs), minimum(Min, Xs) ), labeling([enum], Xs) ). ... we run the following tests: To estimate worst-case overhead of min/max constraint: ?- member(How, [0,v1,v2]), call_time(bench_(How,10,10), T_ms). How = 0 , T_ms = 5910 ; How = v1, T_ms = 19560 ; How = v2, T_ms = 7190. To measure the runtime costs of propagating min/max constraints in more typical cases: ?- between(6, 8, N), NN #= N+N, call_time(bench_(v1,N,NN),T_ms). N = 6, NN = 12, T_ms = 50 ; N = 7, NN = 14, T_ms = 300 ; N = 8, NN = 16, T_ms = 2790. ?- between(6, 8, N), NN #= N+N, call_time(bench_(v2,N,NN),T_ms). N = 6, NN = 12, T_ms = 20 ; N = 7, NN = 14, T_ms = 100 ; N = 8, NN = 16, T_ms = 830. In both "use cases", the specialized constraints give impressive speedup. Footnote 1: Using SICStus Prolog version 4.3.2 (64-bit). Footnote 2: Answer sequences were post-processed to improve appearance.