This question is subsequent to another one I posted earlier on custom labeling in Prolog.
Does the contracting/1 predicate, when used after a value assignment to a variable in a custom labeling predicate, delete the "inconsistent" values from the domain permanently ? Or are these values restored when backtracking ?
These values are of course restored on backtracking.
It is the nature of pure Prolog predicates, such as CLP(FD) constraints, that everything they state is completely undone on backtracking. Without this, many important declarative properties would not hold. See logical-purity for more information.
You can see easily that this also holds for clpfd:contracting/1, using for example a sample session:
?- X in 0..5, X mod Y #= 2, Y in 0..2.
X in 0..5,
X mod Y#=2,
Y in 1..2.
?- X in 0..5, X mod Y #= 2, Y in 0..2, clpfd:contracting([X,Y]).
false.
?- X in 0..5, X mod Y #= 2, Y in 0..2, ( clpfd:contracting([X,Y]) ; true ).
X in 0..5,
X mod Y#=2,
Y in 1..2.
Related
I am looking at some questions in my textbook about whether or not the variables are free or bound. I am not sure about these two in particular.
First off, I want to make sure I understand the concept of free vs. bound. I am fairly sure this x is a free variable in the following:
variable x is free in expression "x"
I believe this is true but I just want to make sure.
These two questions I am not really sure about, however.
(/ (+ 1 x) (let x 2 (+ x x))),
(let x y (/ (+ 1 x) (let x 2 (+ x x))))
For the top expression, the x in the first subexpression is unbound(right?) but x in the second subexpression is bound to 2, so would that mean the x in regards to the expression as a whole is unbound?
For the bottom expression, x is bound to y, but y is a free variable(?). So would x be free because y is free or is it bound because x is still bounded to y?
For (/ (+ 1 x) (let x 2 (+ x x))), the x in the first subexpression is unbound but x in the second subexpression is bound to 2, so would that mean the x in regards to the expression as a whole is unbound?
Yes. Although I would use the terminology "is bound" or "is free" only with respect to a concrete variable expression, not to a name. As you can see, it's ambiguous what "x" refers to.
I'd say "the whole expression has a free variable x", which is what you usually care about when trying to evaluate the expression.
For (let x y (/ (+ 1 x) (let x 2 (+ x x)))), x is bound to y, but y is a free variable. So would x be free because y is free or is it bound because x is still bounded to y?
The x is bound (and can be substituted). y is free.
I want to use Gurobi to solve for a very simple LP:
minimize z
s.t. x + y <= z
where x, y, z are decision variables generated by gp.Model().addVar() which should be the default variable. The objective of the model is set to be m.setObjective(1.0*z, GRB.MINIMIZE).
Then I solved the model, and the program returns the optimal value for z is 0.000. I don't understand why this is the optimal value? Is there any constraint on the default decision variables of Gurobi, like they are non-positive. Otherwise, why 0.0 is the optimal value for this LP when x, y, and z are unbounded?
The convention for Gurobi and other LP/MIP solvers are that decision variables have a lower bound of zero. If you want another lower bound, then either set the LB attribute, or define it when you call Model.addVar(), ex:
m = Model()
x = m.addVar(lb=-20, name='x')
I need to rely on the fact that two Z3 variables
can not have the same name.
To be sure of that,
I've used tuple_example1() from test_capi.c in z3/examples/c and changed the original code from:
// some code before that ...
x = mk_real_var(ctx, "x");
y = mk_real_var(ctx, "y"); // originally y is called "y"
// some code after that ...
to:
// some code before that ...
x = mk_real_var(ctx, "x");
y = mk_real_var(ctx, "x"); // but now both x and y are called "x"
// some code after that ...
And (as expected) the output changed from:
tuple_example1
tuple_sort: (real, real)
prove: get_x(mk_pair(x, y)) = 1 implies x = 1
valid
disprove: get_x(mk_pair(x, y)) = 1 implies y = 1
invalid
counterexample:
y -> 0.0
x -> 1.0
to:
tuple_example1
tuple_sort: (real, real)
prove: get_x(mk_pair(x, y)) = 1 implies x = 1
valid
disprove: get_x(mk_pair(x, y)) = 1 implies y = 1
valid
BUG: unexpected result.
However, when I looked closer, I found out that Z3 did not really fail or anything, it is just a naive (driver) print out to console.
So I went ahead and wrote the exact same test with y being an int sort called "x".
To my surprise, Z3 could handle two variables with the same name when they have different sorts:
tuple_example1
tuple_sort: (real, real)
prove: get_x(mk_pair(x, y)) = 1 implies x = 1
valid
disprove: get_x(mk_pair(x, y)) = 1 implies y = 1
invalid
counterexample:
x -> 1.0
x -> 0
Is that really what's going on? or is it just a coincidence??
Any help is very much appreciated, thanks!
In general, SMT-Lib does allow repeated variable names, so long as they have different sorts. See page 27 of the standard. In particular, it says:
Concretely, a variable can be any symbol, while a function symbol
can be any identifier (i.e., a symbol or an indexed symbol). As a
consequence, contextual information is needed during parsing to know
whether an identifier is to be treated as a variable or a function
symbol. For variables, this information is provided by the three
binders which are the only mechanism to introduce variables. Function
symbols, in contrast, are predefined, as explained later. Recall that
every function symbol f is separately associated with one or more
ranks, each specifying the sorts of f’s arguments and result. To
simplify sort checking, a function symbol in a term can be annotated
with one of its result sorts σ. Such an annotated function symbol is a
qualified identifier of the form (as f σ).
Also on page 31 of the same document, it further clarifies "ambiguity" thusly:
Except for patterns in match expressions, every occurrence of an
ambiguous function symbol f in a term must occur as a qualified
identifier of the form (as f σ) where σ is the intended output sort of
that occurrence
So, in SMT-Lib lingo, you'd write like this:
(declare-fun x () Int)
(declare-fun x () Real)
(assert (= (as x Real) 2.5))
(assert (= (as x Int) 2))
(check-sat)
(get-model)
This produces:
sat
(model
(define-fun x () Int
2)
(define-fun x () Real
(/ 5.0 2.0))
)
What you are observing in the C-interface is essentially a rendering of the same. Of course, how much "checking" is enforced by the interface is totally solver specific as SMT-Lib says nothing about C API's or API's for other languages. That actually explains the BUG line you see in the output there. At this point, the behavior is entirely solver specific.
But long story short, SMT-Lib does indeed allow two variables have the same name used so long as they have different sorts.
I'm looking for a tool for determining whether a given set of linear equations/inequalities (A) entails another given set of linear equations/inequalities (B). The return value should be either 'true' or 'false'.
To illustrate, let's look at possible instances of A and B and the expected return value of the algorithm:
A: {Z=3,Y=Z+2, X < Y} ;
B: {X<5} ;
Expected result: true
A: {Z=3,Y=Z+2, X < Y} ;
B: {X<10} ;
Expected result: true
A: {Z=3,Y=Z+2, X < Y} ;
B: {X=3} ;
Expected result: false
A: {X<=Y,X>=Y} ;
B: {X=Y} ;
Expected result: true
A: {X<=Y,X>=Y} ;
B: {X=Y, X>Z+2} ;
Expected result: false
Typically A contains up to 10 equations/inequalities, and B contains 1 or 2. All of them are linear and relatively simple. We may even assume that all variables are integers.
This task - of determining whether A entails B - is part of a bigger system and therefore I'm looking for tools/source code/packages that already implemented something like that and I can use.
Things I started to look at:
Theorem provers with algebra - Otter, EQP and Z3 (Vampire is currently unavailable for download).
Coq formal proof management system.
Linear Programming.
However, my experience with these tools is very limited and so far I didn't find a promising direction. Any guidelines and ideas from people more experienced than me will be highly appreciated.
Thank you for your time!
I think I found a working solution. The problem can be rephrased as an assignment problem and then it can be solved by theorem provers such as Z3 and with some work probably also by linear programming solvers.
To illustrate, let's look at the first example I gave above:
A: {Z=3, Y=Z+2, X<Y} ;
B: {X<5} ;
Determining whether A entails B is equivalent to determining whether it is impossible that A is true while B is false. This is a simple simple logical equivalence. In our case, it means that rather than checking whether the condition in B follows from the ones in A, we can check whether there is no assignment of X, Y and Z that satisfies the conditions in A and not in B.
Now, when phrased as an assignment problem, a theorem prover such as Z3 can be called for the task. The following code checks if the conditions in A are satisfiable while the one in B is not:
(declare-fun x () Int)
(declare-fun y () Int)
(declare-fun z () Int)
(assert (= z 3))
(assert (= y (+ z 2)))
(assert (< x y))
(assert (not (< x 5)))
(check-sat)
(get-model)
(exit)
Z3 reports that there is no model that satisfies these conditions, thus it is not possible that B doesn't follow from A, and therefore we can conclude that B follows from A.
Is there a way I can create a function for addition/3 which does that:
add(1,2,X) - X is 3.
add(1,X,3) - X is 2.
add(X,2,3) - X is 1.
As a beginner, the best is to use library(clpfd) which provides all that functionality ; and even more than that. With
?- use_module(library(clpfd)).
We start, in SICStus you have now to tell assert(clpfd:full_answer), then we have:
?- 1+2#=Z.
Z = 3.
?- 1+Y#=3.
Y = 2.
as you expected it. But even more than that!
?- X+X#=Z.
2*X#=Z.
?- X+X#=X.
X = 0.
?- X+Y#=Z.
X+Y#=Z.