The CLP(Â) language is an instance of the Constraint Logic Programming scheme defined by Jaffar and Lassez []. Its operational model is similar to that of PROLOG. A major difference is that unification is replaced by a more general mechanism: solving constraints in the domain of uninterpreted functors over real arithmetic terms. A working knowledge of PROLOG programming is assumed in this document, although the book by Sterling and Shapiro [] can serve as a suitable introductory text. Further technical information on CLP(Â) is available on language design and implementation [,], metaprogramming [] and delay mechanisms []. Additionally, much has been written about applications in electrical engineering [], differential equations [], options trading [], music theory [] etc.
This document is both an introductory tutorial and reference manual describing IBM's compilerbased implementation of CLP(Â). Compiled CLP(Â) is an interactive system that compiles all programs and goals into CLAM code which is interpreted by a bytecode emulator that is part of the system. The system is portable in the sense that it will run on virtually all 32 bit Unix machines with a reasonably standard C compiler, as well as many others.
We would like to emphasize that this manual describes a constantlyevolving, experimental system. Hence much of what is described is subject to change in future releases. Furthermore, the use of undocumented features is particularly dangerous.
A program is a collection of rules. The definition of a rule is similar to that PROLOG clause, but it differs in two important ways: rules can contain constraints as well as atoms in the body, and the definition of terms is more general. A goal is a rule without a head, as usual.
The body of a rule may contain any number of arithmetic constraints, separated by commas in the usual way. Constraints are equations or inequalities, built up from real constants, variables, +, , *, /, and , >=, <=, >, < where all of these symbols have the usual meanings and parentheses may be used in the usual way to resolve ambiguity. Unary negation is also available, as are some special interpreted function symbols which will be described later. Any variable that appears in an arithmetic constraint is said to be an arithmetic variable, and cannot take a nonarithmetic value. These constraints may be thought of as builtin predicates written infix, but they are really much more powerful, as we shall see later. Goals are also similar to those in PROLOG, and may contain explicit constraints as well.
An arithmetic expression (also called an arithmetic term) can also be used in constructing terms, as parts of atoms. For example,
X 3.14159 X + Y sin(X + 2.0) ( X + Y ) / 4are all valid arithmetic terms. However,
f(3) c + 5.0 tan(f(3))are not. Furthermore,
X > 5.0 X + Y + Z = 3 X <= Y X = V 3 = tan(X) 1.234 + X < Yare all valid constraints, while
c > Y X = 3.0 < Y pow(X = Y, 3) 4 < X < 5are not. The following terms, some of which contain arithmetic terms, are valid:
f(c) f(X) f(3.14159) g(22, h(4)) f(X + 3) dbut the following are not valid terms
f(X) + g(X) a  3 .
Now we will look at some simple example programs without considering how their execution differs from that of PROLOG programs. The first example is a program expressing the relation fib(N, X) where X is the Nth Fibonacci number.
fib(0, 1). fib(1, 1). fib(N, X1 + X2) : N > 1, fib(N  1, X1), fib(N  2, X2).To compute the 10th Fibonacci number, we can use the goal
? fib(10, Z).while to find out which fibonacci number is 89, we can use the goal
? fib(X, 89).The next program describes the relationship between two complex numbers and their product. We represent the complex number X + iY as c(X,Y).
zmul(c(R1, I1), c(R2, I2), c(R3, I3)) : R3 = R1 * R2  I1 * I2 , I3 = R1 * I2 + R2 * I1 .Any of the following goals will return a unique answer. The first goal asks for the product of two complex numbers, while the other two ask for the result when one complex number is divided by another.
? zmul(c(1, 1), c(2, 2), Z), ? zmul(c(1, 1), Y, c(0, 4)), ? zmul(X, c(2, 2), c(0, 4)),Notice how both operations are described using the definition of complex multiplication, rather than writing a separate rule that divides complex numbers by first realizing the divisor and then multiplying. This declarative aspect will be an important feature of many of the programs we look at. Also notice that both of the programs we have seen so far have been invertible in the sense that it did not matter which terms in the goals were ground and which were not. This is a property that we will try to obtain as often as possible when we define programs or parts of programs. Even the special functions sin, cos, tan, and others are designed with this in mind. For example the constraint
X = tan(Y)serves both to compute the tangent of Y and to compute Y = Tan^{1} X while the latter expression cannot be expressed directly as a constraint.
Similarly, the pow function can be used to compute powers, roots and logarithms of arbitrary base. For example, we may define the rules
sqroot(X, pow(X, 0.5)): X >= 0. sqroot(X, pow(X, 0.5)) X >= 0.which state that a nonnegative number has a positive and negative square root.
Let us consider some examples. We start with a program that has no explicit constraints or arithmetic terms, effectively written in PROLOG.
p(f(c)). q(g(X)) : p(f(X)). ? q(Y).
As the compuation proceeds, the collected constraint set and current goal
are as follows:
{ } ? q(Y).
{ q(Y) = q(g(X)) } ? p(f(X)).
{ q(Y) = q(g(X)), p(f(X)) = p(f(c)) } ? .
Note that only the successful path is shown here. Also, as we will discuss in more
detail later, the ``answer'' to this query is just the set of constraints
collected, but ``projected'' onto the goal variables. So the answer to the
above query is
Y = g(c).Now consider a more general program, which includes both arithmetic terms and explicit constraints:
p(10, 0). q(W, c(U, V)): W  U + V = 10, p(U, V). ? q(Z, c(X + Y, X  Y)).and again we only look at the successful path of the execution:
{} ? q(Z, c(X + Y, X  Y)).
{ q(Z, c(X + Y, X  Y)) = q(W, c(U, V)) } ? W  U + V = 10, p(U, V).
{ q(Z, c(X + Y, X  Y)) = q(W, c(U, V)), W  U + V = 10 } ? p(U, V).
{ ¼, p(U,V) = p(10,10) } ? .
The answer for this derivation is
X = 5, Y = 5, W = 10,and we should notice that, as expected, it does not contain any mention of the variables U, V, and W. Also note that, in general, the answers need not give values to variables, and it is possible to get an answer constraint like
X + Y + Z = 0, X > Y.This facility is a very important and useful feature of CLP(Â) as we will illustrate later.
A knowledge of how CLP(Â) programs are executed, and especially how and when variables are instantiated, is useful for a number of reasons. As is the case for PROLOG, some procedures of a program may have infinite derivations unless certain variables are instantiated. Furthermore, in the CLP(Â) system we need to be aware of the kinds of constraints that can and cannot be solved. To understand these aspects of CLP(Â) fully, it is necessary to understand how the solution of constraints may be delayed by the system.
In the above discussion of the operational model, we saw how each operational step results in one or more constraints being added to the collected constraint set, and the new set being checked for satisfiability. Because of the requirement that an efficient interpreter be available, there is a limit to how sophisticated the decision algorithm for constraints can be, and consequently the collected constraint set may get too complicated for the decision algorithm. In particular, consider a case when the collected constraint set is solvable, but one constraint is added which makes the set so complicated that it is not practical to decide whether it has remained solvable. One approach to dealing with this problem might be to disallow expressions that can result in such complexity. However, in CLP(Â), because of the requirement for generality, such expressions are kept in a delayed constraint set. At each operational step, instead of blindly adding each constraint to the collected constraint set, we remove any constraints that would make the set too complicated, and keep them in the delayed constraint set. Additionally, at each step it is possible that some constraint in the delayed constraint set need no longer be delayed because of new information. In this case it should be moved from the delayed constraint set to the collected constraint set and the usual solvability check made. Note that the notion of which expressions are ``too complicated'' is dependent on the implementation. The particular distinction for CLP(Â) will be discussed later in this section.
First let us consider the example where the collected constraint set is empty, and the constraint obtained is
V = I * R.Then this is placed in the delayed constraint set. Continuing, if the next constraint is
V = 10,it may be added to the collected constraint set, but note that it is still not easy to decide whether the two constraints together are solvable  we are assuming that the best we can do is solve simultaneous linear equations. Now consider what happens if the next constraint is
R = 5.This gives us enough information to make the delayed constraint linear, so we simply place this delayed constraint into the collected constraint set, and check that it is solvable, which of course it is. Note that the delayed constraint set may have contained other constraints, which may have to remain there until much later. Also note that because of this delay mechanism, we may continue through a certain computation sequence even though the collected and delayed constraint sets together are not solvable. In the worst case it can result in an infinite loop. This is the price we pay for an efficient decision algorithm.
In the CLP(Â) system, a linear equation or inequality is always sufficiently simple to be solved immediately, as a solution in parametric form is held internally.
Finally, the functions sin, cos, tan, pow, max, min and abs are delayed until they become simple evaluations in one direction or another. This means that sin, cos and tan require either the input or output to be ground, and pow requires 2 out of 3 to be ground, except in cases such as
X = pow(Y, Z)where Z = 0. The reason is that Y^{0} is 1 for all values of Y. Note that while this is sufficient to determine the value of X, Y remains nonground. There are similar special cases when Z is 1, and when Y is 0 or 1.
In the context of Prolog, metaprogramming refers to the destruction and construction of rules and terms, and the examination and modification of the rulebase. All of the same issues arrise in CLP(Â), but the special nature of arithmetic terms and arithmetic constraints results in some extra facilities being needed, and requires some of the remaining ones to be modified. The modifications are needed to:
First we introduce the macrolike operator quote
.
This is expanded in an
outermost fashion when expressions are first read. The argument of the
quote operator is translated to a version in which all arithmetic operators
are translated to a special coded form, which is not otherwise directly
accessible to the programmer. This coded form is an uninterpreted
function symbol. In this discussion, such coded forms of
arithmetic function symbols will be be represented with a caret over them.
For example, the rule
p(X, Y, quote(X + Y)).
would be read in as
p(X, Y, X [^(+)] Y).
and so on. Furthermore, the quote operator passes through all other
function symbols, constants, variables etc. without changing them. Thus for
example, the rule
q(X,Y) : X = quote(f(g(Y), 2 * Y)).
becomes
q(X,Y) : X = f(g(Y), 2 [^(*)] Y).
Of course, the original form of the rule is always shown when listing the database, etc., but when printing a term, coded function symbols are printed preceded by a caret and surrounded by single quotes. For example, the query ? q(X, 5). to the above rule would yield the answer X = f(g(5), 2 `^*' 5).
Additionally, to facilititate manipulating programs which themselves use metaprogramming facilities, we need coded forms of the quote operator itself, and the new eval interpreted function symbol, which will be described below. This is why quote is expanded outermost first. For example,

Now, the major linguistic feature for metaprogramming with constraints
is the interpreted function symbol eval
which converts a coded term to the term it codes. It passes through
uninterpreted function symbols other than those that are coded forms of
interpreted ones, without changing them. Likewise for constants and
interpreted function symbols. However, it is delayed until its argument is
constructed. So, for example, the goal
? X = f(a, g(c)), U = eval(X).
results in both U and X being f(a, g(c)). However,
? X = f(Y, g(c)), U = eval(X).
results in U being f(eval(Y), g(c)), as the ``best'' representation of terms
containing eval is that with eval pushed inwards as far as
possible. Now consider a more useful goal
? X = quote(U + 1), eval(X) = 5, Y = eval(U)  5.
here after the first constraint X is equal to U [^(+)] 1, but after
the second constraint, eval goes as far through X as it can, so we
obtain the simplified constraint eval(U) + 1 = 5 which is further
simplified to eval(U) = 4. Hence the third constraint results in Y
being 1. Of course, if the goal is permuted to
? eval(X) = 5, Y = eval(U)  5, X = quote(U + 1).
the final answer is the same. However, the first and second constrints both result in delayed eval constraints. The third constraint wakes the first delayed eval since X is now constructed, resulting in the constraint eval(U) + 1 = 5 again, which, together with the second delayed eval constraint  which is not woken  results in Y being grounded to 1 again.
As a final example, consider the goal
? eval(X) + eval(Y) = 4, eval(X)  eval(Y) = 1.
which is rather silly in isolation, but could arise as the result of a longer copmputation. In this case, the answer constraints are eval(X) = 2.5, eval(Y) = 1.5 although the values of X and Y cannot be determined uniquely. For example, X might be 2.5, or 1 [^(+)] 1.5, etc.
As we shall see later, the CLP(Â) system incorporates a system predicate dump/1 which prints the constraints on the list of variables provided as its argument. More generally, in metaprograaming it can be useful to obtain the coded form of the constraints on a number of variables. For this we need another system predicate dump/3 . There are three arguments because it is not sufficient to simply provide the variables to be projected upon (1st argument) and the variable that receives the coded form (3rd argument). The 2nd argument is a list of terms that are to replace the original variables in the coded form, and hence the lengths of the two lists must be the same. There are two reasons for this. First, it is very inconvenient to manipulate a coded form containing variables that have the original arithmetic constraints still imposed on them  in particular, printing such a term leads to highly counterinuitive results. Second, in many cases it is more convenient to manipulate ground representations of the coded forms. That is, with syntactic constants replacing the variables. The terms resulting from manipulation can then have the original (or other) variables substituted into place easily.
Let us consider an example. We will assume that the predicate p/2 sets up a constraint such that the first argument is a (polynomial) function of the second, and that diff/2 implements symbolic differentiation on coded forms of arithmetic constraints. Then, to find the turning point of the functional relationship established by p/2 we can use the following goal:
solve(DYDX,X): eval(DYDX) = 0. p(Y,X): T = X + 1, Y = T * T. ? p(Y,X), /*** Collects a function Y(X) ***/ dump([Y, X], [V, U], Z), /*** Accesses the coded form of Y(X) ***/ Z =.. ['=', V, RHS], /*** Assumes Z is one equation V = f(U) ***/ diff(RHS, DVDU), /*** Symbolic differentiation ***/ solve(DVDU, U), /*** Finds extremum ***/ printf("Turning point: X = %, Y = % \n",[U,V]).
Next we consider how these basic facilities may be used for reasoning about programs. The canonical application for such reasoning is the metacircular interpreter. We begin by discussing the simple (``vanilla'') meta interpreter and then consider extensions to this interpreter that provide (in varying degrees) their own constraint solving algorithms.
Like the clause/2 predicate of Prolog, we need the system predicate rule/2 such that the goal ? rule(H,B) behaves as if there were facts rule(E,F) for each rule E : F in the program (and of course rule(A,true) for each fact A.) Then the basic metainterpreter may be written:
goal(true). goal((A, B)) : goal(A), goal(B). goal(X) : constraint(X). goal(X) : rule(X,Z), goal(Z). constraint(A = B): A = B. constraint(A > B): A > B. /* similarly for <, <=, >= */The goal ? g. is solved by running ? goal(g). There are two important points here. First, this interpreter utilizes the constraint solver of the underlying CLP(Â) system  it takes no control over how the constraints are solved. Second, the interpreter deals correctly with programs that contain occurrences of quote and eval.
Since rule returns terms rather than coded terms, it cannot be used to examine the rulebase structurally. This essentially prevents us from exercising any control over the constraint solving process. To overcome this restriction we need to be able to obtain coded forms of terms in the rule base. For this reason we need the system predicate quoted_rule/2 which behaves as if there were facts quoted_rule(quote(E),quote(F)) for each rule E : F in the rulebase (and quoted_rule(quote(A),true) for each fact A). We note that rule/2 can be written in terms of quoted_rule/2 :
rule(eval(H),eval(B)) : quoted_rule(H,B).We now modify the basic metainterpreter to use quoted_rule instead of rule as follows: (We note that for pragmatic reasons an implementation of quoted_rule would require that its first argument be constructed, requiring minor alterations to this metainterpreter.)
goal(true). goal((A, B)) : goal(A), goal(B). goal(X) : constraint(X). goal(X) : constraint(X = Y), quoted_rule(Y,Z), goal(Z). constraint(A = B): eval(A) = eval(B). constraint(A > B): eval(A) > eval(B). /* similarly for <, <=, >= */
In this case the goal ? g. is solved by running ? goal(quote(g)). Again, this interpreter may be used to execute programs which use the quote and eval facilities, including itself. (We would, of course, require an extra rule to execute the system predicate quoted_rule .) It is now possible to extend this interpreter to give more control over the solving of constraints by replacing the rules for , >= , <= , > , < by new constraint solving algorithms.
It is desirable to be able to selectively delete rules from the database on the basis of the coded form of the database as well as the actual database. For this reason we need the additional predicate quoted_retract/1 to complement retract/1. For example consider the following program:
(a) p(1, 1.5). (b) p(X, Y) : Y = 2*X. (c) p(X, 2*X). (d) p(X/2, X).
The goal ? retract(p(X, 2*X)) removes rules (c) and (d). However the goal ? quoted_retract(quote(X, 2*X)) removes only the rule (c). Rule (b) could be removed with the goal ? retract(p(X, Y) : W) . The relationship between retract and quoted_retract is identical to that between rule and quoted_rule . In particular, retract/1 can be implemented in terms of quoted_retract/1 :
retract(eval(R)) : quoted_retract(R).
In the presence of constraints, the definition of assert/1 is somewhat unclear. The essential question is how to treat arithmetic constraints on the variables in the term to be asserted. For example, consider the goal
? X + Y > 2, assert( p(X, Y) ).
To motivate our definition of assert we examine the behavior of assert in Prolog in terms of constraints. Consider the following Prolog program and query:
p(f(U, d), e). q(f(c, Z)). ? p(X, Y), q(X), assert( r(X) : s(Y) ).The expected result in Prolog is that the rule
r(f(c,d)) : s(e).is added to the database, since this corresponds to the variable bindings when assert is called. Equivalently, the call to assert may be viewed as the call assert(r(X) : s(Y)) in the presence of the constraints
X = f(U,d) & Y = e & X = f(c,Z).
Simplifying these constraints, and projecting onto the variables occurring in the argument to assert , we have
X = f(c,d) & Y = e.If we combine the rule r(X) : s(Y) with a representation of these constraints we obtain:
r(X) : X = f(c,d), Y = e, s(Y).which is semantically equivalent to the expected result in Prolog. In Prolog, constraints may always be represented as a substitution, and so the explicit appearance of constraints in an asserted rule is unnecessary. For example we may represent the constraints { X = f(c,d), Y = e. } as the substitution { X/f(c,d), Y/e }, and on applying this to the rule, obtain the expected Prolog result. However this is not the case for arithmetic constraints, and so the explicit appearance of constraints in an asserted rule is necessary.
We therefore propose that assert(h : b_{1}, ¼, b_{n}) adds the rule

For example, the goal ? X + Y > 2, assert( p(X, Y) ) , given earlier, asserts the rule:
p(X, Y) : X + Y > 2.As another example, consider the goal:
? X + Y = 2, X >= 0, Y  2*X <= 2, X > W, Y  X >= 1, assert( p(X, Y) ).which asserts the rule:
p(X,Y) : X + Y = 2, Y >= 1.5, Y <= 2.Note that a considerable simplification of the initial constraints has occurred. More generally, this supports a technique of constraint partial evaluation. This technique consists of executing a query, and then using the simplified form of the answer constraints to construct new rules. These new rules represent a specialization of the program with respect to that query. For example:
resistor(V, I, R) : V = I * R. ? resistor(V, I1, R1), resistor(V, I2, R2), I = I1 + I2, assert( parallel_resistors(V, I, R1, R2) ).results in the assertion of a rule describing the voltagecurrent relationship of a pair of resistors connected in parallel:
parallel_resistors(V, I, R1, R2) : I = V / R1 + V / R2.
The facilities we have discussed for adding rules to the database have provided no control over the exact syntax of the rule added. For example constraints may be simplified and/or rearranged before the rule is added. It is particularly important in some applications to have complete control over the syntax of rules added to the database. This control is provided by using a coded form of the rule to be asserted, where assert of a coded rule is defined to add the rule that is coded. For example, the goal
? assert( quote( p(X, X + X) : X  3 > 0 ) ).asserts the rule
p(X, X + X) : X  3 > 0In constrast the goal ? assert( p(X, X + X) : X  3 > 0 ) could, for example, add the (semantically equivalent) rule:
p(X, Y) : Y = 2*X, Y > 6.
Here we collect a number of mediumsized programs that serve to illustrate some interesting program techniques.
Consider one of the standard cryptoarithmetic puzzles. We require an assignment of digits to the letters S, E, N, D, M, O, R, Y such that the equation
S E N D + M O R E  M O N E Yholds. The program first imposes certain constraints on the values. Then axioms are used a generators of possible values. The problem is so combinatorially explosive that a naive generate and test solution could not be contemplated, while the CLP(Â)program runs quite quickly, although the speed depends on the ordering of the generating axioms, dig and carry.
solve(S, E, N, D, M, O, R, Y) : S > 0, E >= 0, N >= 0, D >= 0, M > 0, O >= 0, R >= 0, Y >= 0, S <= 9, E <= 9, N <= 9, D <= 9, M <= 9, O <= 9, R <= 9, Y <= 9, D + E = Y + 10*C1, C1 + N + R = E + 10*C2, C2 + E + O = N + 10*C3, C3 + S + M = O + 10*M, carry(C1, C2, C3), dig(S), dig(E), dig(N), dig(D), dig(M), dig(O), dig(R), dig(Y), difflist([S, E, N, D, M, O, R, Y]). carry(0, 0, 0). carry(0, 0, 1). carry(0, 1, 0). carry(0, 1, 1). carry(1, 0, 0). carry(1, 0, 1). carry(1, 1, 0). carry(1, 1, 1). dig(0). dig(1). dig(2). dig(3). dig(4). dig(5). dig(6). dig(7). dig(8). dig(9). difflist([X  T]) : notmem(X, T), difflist(T). difflist([]). notmem(X, [Y  Z]) : X < Y, notmem(X, Z). notmem(X, [Y  Z]) : X > Y, notmem(X, Z). notmem(X, []). ? solve(S, E, N, D, M, O, R, Y),
The search space is pruned even more if linear inequalities are decided rather than delayed, but in this particular program this results in no further speed improvement because of overheads.
This program uses local propagation to compute start, completion and float times for a project network. Significantly, the constraint paradigm allows the program to compute these values by making only one pass of the project network, as opposed to the threre passes that would be needed using a conventional programming language.
% % Network is an input project network of the form % [ [node1 , node2, time ] .... ] % Graph is the critical path graph produced % Latest is the latest possible completion time is specified % cpm/3 is used if the latest time is specified % otherwise use cpm/2 % cpm(Network,Graph,Latest) : build(Network,Graph), early_late(Graph,Graph,End,Latest), Latest >= End, analyse(Graph,Graph). cpm(Network,Graph) : build(Network,Graph), early_late(Graph,Graph,End), analyse(Graph,Graph). % build an adjacency graph out of the network build([],Graph) : buildv([],_,Graph). build([[I,J,C]T],Graph) : buildv(ed(I,J,C),to,Graph), buildv(ed(I,J,C),from,Graph), build(T,Graph). buildv([],_,[]) : !. buildv([],_,[ad(_,_,_,To,From)T]) : !,addedg([],_,To), addedg([],_,From), buildv([],_,T). buildv(ed(I,J,C),to,[ad(I,Es,Lc,To,From)T]) : !,addedg(J,C,To). buildv(Edge,to,[HT]) : !,buildv(Edge,to,T). buildv(ed(I,J,C),from,[ad(J,Es,Lc,To,From)T]) : !,addedg(I,C,From). buildv(Edge,from,[HT]) : !,buildv(Edge,from,T). addedg([],_,[]) : !. addedg([],_,[HT]) : !, addedg([],_,T). addedg(V,C,[ed(V,C,_,_,_,_)T]) : !. addedg(V,C,[HT]) : addedg(V,C,T). % Get early start times and latest completion times % early/4 is used when a ending time is given % otherwise early/3 assumes that the early start time % for the end node is equal to the latest completion time early_late([],_,_,_). early_late([ad(I,Es,Lc,To,From)T],G,End,Latest) : setearly(From,To,G,End,Es), setlate(To,G,Latest,Lc), early_late(T,G,End,Latest). early_late([],_,_). early_late([ad(I,Es,Lc,To,From)T],G,End) : setearly(From,To,G,End,Es), setlate(To,G,End,Lc), early_late(T,G,End). setearly([],_,_,_,0). setearly([ed(V,C,_,_,_,_)T],[],G,Es,Es) : !, getnode(V,G,Es1,_), setmax(T,G,Es1+C,Es). setearly([ed(V,C,_,_,_,_)T],_,G,End,Es) : getnode(V,G,Es1,_), setmax(T,G,Es1+C,Es). setmax([],_,Max,Max). setmax([ed(V,C,_,_,_,_)T],G,Max0,Max) : getnode(V,G,Es1,_), setmax(T,G,max(Max0,Es1+C),Max). setlate([],_,Last,Last). setlate([ed(V,C,_,_,_,_)T],G,Last,Lc) : getnode(V,G,_,Lc1), setmin(T,G,Lc1C,Lc). setmin([],_,Min,Min). setmin([ed(V,C,_,_,_,_)T],G,Min0,Min) : getnode(V,G,_,Lc1), setmin(T,G,min(Min0,Lc1C),Min). % Search graph for the early & late times for a node getnode(I,[ad(I,Es,Lc,_,_)T],Es,Lc). getnode(I,[HT],Es,Lc) : getnode(I,T,Es,Lc). % Compute the other times : % Ls  latest start time % Ec  earliest completion time % Tf  total float time % Ff  free float time analyse([],G). analyse([ad(I,Es,Lc,To,_)T],G) : analyse_times(To,Es,Lc,G), analyse(T,G). analyse_times([],_,_,_). analyse_times([ed(V,C,Ls,Ec,Tf,Ff)T],Esi,Lci,G) : getnode(V,G,Esj,Lcj), compute(Ls,Ec,Tf,Ff,Esj,Lcj,Esi,Lci,C), analyse_times(T,Esi,Lci,G). % Indirect way of doing the calculation just to speed things up % can be removed and inserted directly in analyse_times compute(Ls,Ec,Tf,Ff,Esj,Lcj,Esi,Lci,C) : X = Esi+C, Ls = LcjC, Ec = Esi+C, Tf = LcjX, Ff = EsjX. % display routines print_analysis(G) : printf("\t\tNode\tEs\tLc\n",[]), printf("Node1\tNode2\tT\tLs\tEc\tTf\tFf\n",[]), print_analysis1(G). print_analysis1([]). print_analysis1([HT]) : print_node(H), print_analysis1(T). print_node(ad(I,Es,Lc,[],From)) : !, printf("\n",[]), printf("END NODE\t%\t%\t%\n",[I,Es,Lc]). print_node(ad(I,Es,Lc,To,[])) : !, printf("\n",[]), printf("START NODE\t%\t%\t%\n",[I,Es,Lc]), printf("\n",[]), print_times(To,I). print_node(ad(I,Es,Lc,To,From)) : printf("\n",[]), printf("\t\t%\t%\t%\n",[I,Es,Lc]), printf("\n",[]), print_times(To,I). print_times([],_). print_times([ed(V,C,Ls,Ec,Tf,Ff)T],I) : printf("%\t%\t%\t%\t%\t%\t%",[I,V,C,Ls,Ec,Tf,Ff]), is_critical(Tf), print_times(T,I). is_critical(0) : printf(" *\n",[]). is_critical(Tf) : Tf > 0, printf("\n",[]). % Explanation of output /* Node Es Lc ( Gives the Earliest Start and Latest Completion timed for the node) Node1 Node2 T Ls Ec Tf Ff (Details the times relating to the activity between Node1 & Node2 T is the time required for the activity Ls the Latest Start time Ec the Earliest Completion time Tf the Total Float Ff the Free Float) Activities on the critical path are marked with an asterisk The start node and end node are computed automatically and distinguished */Now the goal
? cpm([ [n1,n2,4],[n1,n3,3],[n1,n4,4], [n2,n5,7],[n2,n3,1],[n2,n7,8], [n3,n5,4], [n4,n6,2], [n5,n6,1],[n5,n7,3], [n6,n7,4]],G), print_analysis(G).results in the output
Node Es Lc Node1 Node2 T Ls Ec Tf Ff  START NODE n1 0 0  n1 n2 4 0 4 0 0 * n1 n3 3 4 3 4 2 n1 n4 4 6 4 6 0  n2 4 4  n2 n5 7 4 11 0 0 * n2 n3 1 6 5 2 0 n2 n7 8 8 12 4 4  n3 5 7  n3 n5 4 7 9 2 2  n4 4 10  n4 n6 2 10 6 6 6  n5 11 11  n5 n6 1 11 12 0 0 * n5 n7 3 13 14 2 2  END NODE n7 16 16  n6 12 12  n6 n7 4 12 16 0 0 *
The user interface of compiled CLP(Â) is very much like that of a usual Edinburghstyle Prolog interpreter. In other words, it is quite possible to use this system while almost completely ignoring the fact that it is compilerbased. No distinction is made between compiled and interpreted code. All goals are compiled (quickly) before being executed, and any consulted file is immediately compiled. Normally the rulebase is available for inspection and can be modified dynamically as long as the relevant relations have been declared to be dynamic as described below. Normally the user will find that consulted files take a little longer than usual to be read in (because they are being compiled) and that programs will usually run much more quickly and use less space than in an interpreter. Symbolic debugging is still possible, as are all other aspects of interactive programming. However, the user may also take special advantage of the compiler by creating clam files that contain compiled CLP(Â) code that can be loaded extremely quickly and do not include the overhead of the original program text, although this rules out certain operations. In short, the system is intended to get the best of both worlds by combining the flexibility of an interpreter with the efficiency of a compiler.
Note: Compilation into CLAM files has not yet been implemented.
In addition to the name of a file to be consulted on startup, the following command line options are available:
After the system has been initialized, it will prompt the user for a query. This again is similar to the style of most PROLOG systems. Once a query has been solved, the constraints on variables in the query are printed and the user is prompted to either press carriage return or enter ``.'' (or ``n'') to accept the answers, or ``;'' (or ``y'') to cause backtracking. Each successive set of answer constraints is printed in this way. If there is no chance of backtracking (ie: no choice points), the user is simply informed whether the goal succeeded. Note that if delayed constraints remain at the end of the execution, the answer Maybe is returned. A buffer of the last 50 goals is kept, and may be examined by the h predicate. An old query may be reexecuted by just entering its history number as a goal (eg: ? 5.).
The concept of answer constraints for a query, rather than simply an answer substitution, needs to be discussed in more detail. In PROLOG, all the substitutions obtained throughout the execution of a query are ``projected'' onto the goal variables, and the result is printed. If all variables in a CLP(Â) goal have either ground values or strictly syntactic bindings, the result is the same. However, when some goal variables are involved in arithmetic constraints such that the variables do not become ground, the situation is a generalisation of the answer substitution case. Consider the example:
p(X): X > 2*X, X < Y. ? p(V).V is not grounded, but involved in two arithmetic constraints. However, there is only one answer constraint:
0 > V.This is because the constraint relating X to Y in the rule is not of interest, as Y does not relate X back to a goal variable in any other way. As another example, consider the two rules
p(X,Y): X + Y = 3. q(X,Y): X + Y + W = 3.and the two goals
? p(A,B). ? q(A,B).The first goal gives the answer constraint
A = 3  Bwhile there is no answer constraint for the second. This is easily understood if we realise that the first rule defines a correspondence between its two arguments, while the second one does not. In particular, the second rule could be called with any two arithmetic values as arguments and would succeed. Consequently, it is only the first rule that imposes a ``constraining'' relationship among the two variables.
The main reason why it is important to understand this behaviour is that a dump predicate, as described below, is provided. This allows the programmer to request a set of ``answer'' constraints at any point of the computation, specifying with respect to which variables the constraints collected so far are to be projected. Consider two rules which show the importance of the choice of variables in the projection:
g: X + Y + Z = 3, dump([X, Y, Z]). g: X + Y + Z = 3, dump([X, Z]).As expected, the first rule prints the answer constraint
X = 3  Y  Z,but the second produces no answer constraint at all: again there is no ``constraining'' relationship between the variables X and Z, or for that matter, any other pair of variables in the rule.
This is a sample session with the CLP(Â) interpreter. Some extra information is given using comments after the % character.
% clpr % Sometimes need switches and arguments. CLP(R) Version 1.1 (c) Copyright International Business Machines Corporation 1989, 1991 All Rights Reserved 1 ? f(X,Y) = f(g(A),B). % some simple ``unification'' Y = B X = g(A) *** Yes 2 ? X = Y + 4 , Y = Z  3, Z = 2. % simple arithmetic evaluation X = 3 Y = 1 Z = 2 *** Yes 3 ? X + Y < Z, 3 * X  4 * Y = 4, 3 * X + 2 * Y = 1. X = 0.666667 Y = 0.5 Z > 0.166667 *** Yes 4 ? X + Y < Z, 3 * X  4 * Y = 4, 2 * X + 3 * Z = 1. X = 1.5*Z + 0.5 Y = 1.125*Z  0.625 Z > 0.0344828 *** Yes 5 ? history. 1 f(X, Y) = f(g(A), B). 2 X = Y + 4, Y = Z  3, Z = 2. 3 X + Y < Z, 3 * X  4 * Y = 4, 3 * X + 2 * Y = 1. 4 X + Y < Z, 3 * X  4 * Y = 4, 2 * X + 3 * Z = 1. *** Yes 6 ? 2. % run second goal again X = Y + 4, Y = Z  3, Z = 2. X = 3 Y = 1 Z = 2 *** Yes 7 ? ['examples/fib']. % consult (load) a program >>> Sample goal: go/0 *** Yes 8 ? ls fib. % look at the program fib(0, 1). fib(1, 1). fib(N, X1 + X2): N > 1, fib(N  1, X1), fib(N  2, X2). *** Yes 9 ? fib(5,F). % only one answer to this F = 8 *** Retry? ; *** No 10 ? F > 7, F < 9, fib(N,F). % only ask for the first answer F = 8 N = 5 *** Retry? 11 ? [`'examples/mortgage']. % use "`" to reconsult >>> Sample goals: go1/0, go2/0 *** Yes 12 ? ls. % look at the entire rulebase fib(0, 1). fib(1, 1). fib(N, X1 + X2): N > 1, fib(N  1, X1), fib(N  2, X2). go: printf("\nFib(14) = ", []), ztime, fib(14, X), ctime(T1), printf("% (Time = %)\n", [X, T1]), printf("Fib1(610) = ", []), ztime, fib(Y, 610), ctime(T2), printf("% (Time = %)\n", [Y, T2]). mg(P, T, I, B, MP): T = 1, B = P + P * I  MP. mg(P, T, I, B, MP): T > 1, mg(P * 1 + I  MP, T  1, I, B, MP). go1: ztime, mg(999999, 360, 0.01, 0, M), ctime(T), printf("Time = %, M = %\n", [T, M]). go2: ztime, mg(P, 720, 0.01, B, M), ctime(T), printf("Time = %\n", [T]), dump([P, B, M]). *** Yes 13 ? [`'examples/mortgage']. >>> Sample goals: go1/0, go2/0 *** Yes 14 ? ls. fib(0, 1). fib(1, 1). fib(N, X1 + X2): N > 1, fib(N  1, X1), fib(N  2, X2). go: printf("\nFib(14) = ", []), ztime, fib(14, X), ctime(T1), printf("% (Time = %)\n", [X, T1]), printf("Fib1(610) = ", []), ztime, fib(Y, 610), ctime(T2), printf("% (Time = %)\n", [Y, T2]). mg(P, T, I, B, MP): T = 1, B = P + P * I  MP. mg(P, T, I, B, MP): T > 1, mg(P * 1 + I  MP, T  1, I, B, MP). go1: ztime, mg(999999, 360, 0.01, 0, M), ctime(T), printf("Time = %, M = %\n", [T, M]). go2: ztime, mg(P, 720, 0.01, B, M), ctime(T), printf("Time = %\n", [T]), dump([P, B, M]). *** Yes 15 ? go2. Time = 0.24 P = 0.000773768*B + 99.9226*M *** Retry? 16 ? [user]. p(X) : writeln(X). ^D *** Yes 17 ? p(hello). hello *** Yes
Slightly more care than usual must be taken in organizing program files in compiled CLP(Â). A file consists of a number of chunks. Each chunk consists of a zero or more rules (defined in the usual way) possibly followed by a goal. That is, a goal always closes off a chunk, and the end of the file closes off the last chunk if a goal has not done so. A relation may not span more than one chunk unless it has been declared to be dynamic (see below) before the first rule defining it. If a relation is defined statically in more than one chunk, the second definition hides the first for all future bindings. However, if the earlier definition has been protected (using the prot/2 predicate) a warning is printed and the new definition is ignored.
The motivation for this restriction is that the state of the rulebase needs to be well defined whenever a goal is encountered in the consulted file.
There may be three kinds of goals in any consulted file. All three kinds are considered to be identical (and behave in the usual way) when they are encountered in a source file that is being consulted. However, they are different when a source file is first to be compiled and the .clam file is to be consulted. In the latter case, all goals of the form : goal are only executed during the compilation stage. Those of the form :: goal are only executed during the consultation of the compiled code, and the goals of the traditional form ? goal are executed twice: once during compilation and once during consultation. The first kind of goal might be used for compiler directives and messages to whoever is watching while some code is being compiled. The second kind might be used for making a program run itself straight after it is loaded. Finally, the third kind of goal is useful for things like operator declarations which need to be present for the remainder of a program to parse correctly and also when the program is running so that terms will print correctly, etc.
The debugging facilities in this version of CLP(Â) are rudimentary.
Because this implementation of CLP(Â) makes use of double precision floating point arithmetic, some problems may be caused by artefacts such as roundoff. The most common problem is that a constraint used as a test (in that all variables are ground) unexpectedly fails because of roundoff. This is dealt with by adjusting the amount of slack that the system allows in numerical compartsisons, using the z command line option.
Nonground variables are printed in one of the following formats:
Input/Output facilies are as follows.
:: op(21, fy, ''). :: op(21, yfx, *). :: op(21, yfx, /). :: op(31, yfx, ()). :: op(31, yfy, +). :: op(37, xfx, <). :: op(37, xfx, <=). :: op(37, xfx, >). :: op(37, xfx, >=). :: op(40, xfx, =). :: op(40, xfx, =..). :: op(40, xfx, is). :: op(50, fx, `). :: op(51, xfy, (.)). :: op(60, fx, alisting). :: op(60, fx, als). :: op(60, fx, h). :: op(60, fx, history). :: op(60, fx, lib). :: op(60, fx, libdir). :: op(60, fx, listing). :: op(60, fx, ls). :: op(60, fx, not). :: op(60, fx, once). :: op(252, xfy, ','). :: op(253, xfy, ;). :: op(254, xfy, (>)). :: op(255, fx, (:)). :: op(255, fx, (::)). :: op(255, fx, (?)). :: op(255, xfx, (:)).
Here we only list those facilities from the Monash interpreter that are no longer supported.
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