F(x1) > a;
F(x2) < b;
∀t, F'(x) >= 0 (derivative) ;
F(x) = ∑ ci*x^i; (i∈[0,n] ; c is a constant)
Your question is quite ambiguous, and stack-overflow works the best if you show what you tried and what problems you ran into.
Nevertheless, here's how one can code your problem for a specific function F = 2x^3 + 3x + 4, using the Python interface to z3:
from z3 import *
# Represent F as a function. Here we have 2x^3 + 3x + 4
def F(x):
return 2*x*x*x + 3*x + 4
# Similarly, derivative of F: 6x^2 + 3
def dF(x):
return 6*x*x + 3
x1, x2, a, b = Ints('x1 x2 a b')
s = Solver()
s.add(F(x1) > a)
s.add(F(x2) < b)
t = Int('t')
s.add(ForAll([t], dF(t) >= 0))
r = s.check()
if r == sat:
print s.model()
else:
print ("Solver said: %s" % r)
Note that I translated your ∀t, F'(x) >= 0 condition as ∀t. F'(t) >= 0. I assume you had a typo there in the bound variable.
When I run this, I get:
[x1 = 0, x2 = 0, b = 5, a = 3]
This method can be generalized to arbitrary polynomials with constant coefficients in the obvious way, but that's mostly about programming and not z3. (Note that doing so in SMTLib is much harder. This is where the facilities of host languages like Python and others come into play.)
Note that this problem is essentially non-linear. (Variables are being multiplied with variables.) So, SMT solvers may not be the best choice here, as they don't deal all that well with non-linear operations. But you can deal with those problems as they arise later on. Hope this gets you started!
Related
Consider a set of constraints F = [a + b > 10, a*a + b + 10 < 50].
When I run it using:
s = Solver()
s.add(F)
s.check()
I get sat solution.
If I run it with:
s = Solver()
s.check(F)
I get an unknown solution. Can someone explain why this is happening?
Let's see:
from z3 import *
a = Int('a')
b = Int('b')
F = [a + b > 10, a*a + b + 10 < 50]
s = Solver()
s.add(F)
print (s.check())
print (s.model())
This prints:
sat
[b = 15, a = -4]
That looks good to me.
Let's try your second variant:
from z3 import *
a = Int('a')
b = Int('b')
F = [a + b > 10, a*a + b + 10 < 50]
s = Solver()
print (s.check(F))
print (s.model())
This prints:
sat
[b = 7, a = 4]
That looks good to me too.
So, I don't know how you're getting the unknown answer. Maybe you have an old version of z3; or you've some other things in your program you're not telling us about.
The important thing to note, however, is that s.add(F); s.check() AND s.check(F) are different operations:
s.add(F); s.check() means: Assert the constraints in F; check that they are satisfiable.
s.check(F) means: Check that all the other constraints are satisfiable, assuming F is. In particular, it does not assert F. (This is important if you do further asserts/checks later on.)
So, in general these two different ways of using check are used for different purposes; and can yield different answers. But in the presence of no other assertions around, you'll get a solution for both, though of course the models might be different.
Aside One reason you can get unknown is in the presence of non-linear constraints. And your a*a+b+10 < 50 is non-linear, since it does have a multiplication of a variable by itself. You can deal with that either by using a bit-vector instead of an Int (if applicable), or using the nonlinear-solver; which can still give you unknown, but might perform better. But just looking at your question as you asked it, z3 is just fine handling it.
To find out what is going on within s.check(F), you can do the following:
from z3 import *
import inspect
a = Int('a')
b = Int('b')
F = [a + b > 10, a*a + b + 10 < 50]
s = Solver()
print (s.check(F))
print (s.model())
source_check = inspect.getsource(s.check)
print(source_check)
The resulting output:
sat
[b = 10, a = 1]
def check(self, *assumptions):
"""Check whether the assertions in the given solver plus the optional assumptions are consistent or not.
>>> x = Int('x')
>>> s = Solver()
>>> s.check()
sat
>>> s.add(x > 0, x < 2)
>>> s.check()
sat
>>> s.model().eval(x)
1
>>> s.add(x < 1)
>>> s.check()
unsat
>>> s.reset()
>>> s.add(2**x == 4)
>>> s.check()
unknown
"""
s = BoolSort(self.ctx)
assumptions = _get_args(assumptions)
num = len(assumptions)
_assumptions = (Ast * num)()
for i in range(num):
_assumptions[i] = s.cast(assumptions[i]).as_ast()
r = Z3_solver_check_assumptions(self.ctx.ref(), self.solver, num, _assumptions)
return CheckSatResult(r)
The semantics of assumptions vs. assertions are discussed here and here. But if have to admit that they are not really clear to me yet.
I would like to set the initial value for variables in z3py in an efficient way.
x,y = Ints(x,y)
s = Solver()
s.add(x>10)
s.check()
s.model()
I would expect the output value is e.g., x = 11, y = 0, not the result x = 11, y = 7.
One way to do it is:
x,y = Ints(x,y)
s = Optimize()
s.add_soft(x==0)
s.add_soft(y==0)
s.add(x>10)
s.check()
s.model()
But it takes much computation time as my program contains many of variables. Any better way to do it?
The slow-down is because you're forcing the optimizer to run, which is an overkill for this purpose. (The optimizing solver can handle max-sat problems, which does the job here, but it is costly and not needed for this case.)
Instead, simply walk over the model and see if there's an assignment for it:
from z3 import *
def model_with_zeros(s, vs):
m = s.model()
result = []
for v in vs:
val = m.eval(v)
if val.eq(v):
result.append((v, 0))
else:
result.append((v, val))
return result
x, y = Ints('x y')
s = Solver()
s.add(x > 10)
print s.check()
print model_with_zeros(s, [x, y])
This prints:
sat
[(x, 11), (y, 0)]
Note that you have to explicitly pass the solver and the variables you are interested in to the model_with_zeros function; as the trick here is precisely to see which variables the solver left untouched.
If you want a different initial value, then you can modify model_with_zeros to account for that for each variable separately.
I've been working on a project that renders a Mandelbrot fractal. For those of you who know, it is generated by iterating through the following function where c is the point on a complex plane:
function f(c, z) return z^2 + c end
Iterating through that function produces the following fractal (ignore the color):
When you change the function to this, (z raised to the third power)
function f(c, z) return z^3 + c end
the fractal should render like so (again, the color doesn't matter):
(source: uoguelph.ca)
However, when I raised z to the power of 3, I got an image extremely similar as to when you raise z to the power of 2. How can I make the fractal render correctly? This is the code where the iterations are done: (the variables real and imaginary simply scale the screen from -2 to 2)
--loop through each pixel, col = column, row = row
local real = (col - zoomCol) * 4 / width
local imaginary = (row - zoomRow) * 4 / width
local z, c, iter = 0, 0, 0
while math.sqrt(z^2 + c^2) <= 2 and iter < maxIter do
local zNew = z^2 - c^2 + real
c = 2*z*c + imaginary
z = zNew
iter = iter + 1
end
So I recently decided to remake a Mandelbrot fractal generator, and it was MUCH more successful than my attempt last time, as my programming skills have increased with practice.
I decided to generalize the mandelbrot function using recursion for anyone who wants it. So, for example, you can do f(z, c) z^2 + c or f(z, c) z^3 + c
Here it is for anyone that may need it:
function raise(r, i, cr, ci, pow)
if pow == 1 then
return r + cr, i + ci
end
return raise(r*r-i*i, 2*r*i, cr, ci, pow - 1)
end
and it's used like this:
r, i = raise(r, i, CONSTANT_REAL_PART, CONSTANT_IMAG_PART, POWER)
Over the years I keep track of solving technology - and I maintain a blog post about applying them to a specific puzzle - the "crossing ladders".
To get to the point, I accidentally found out about z3, and tried putting it to use in the specific problem. I used the Python bindings, and wrote this:
$ cat laddersZ3.py
#!/usr/bin/env python
from z3 import *
a = Int('a')
b = Int('b')
c = Int('c')
d = Int('d')
e = Int('e')
f = Int('f')
solve(
a>0, a<200,
b>0, b<200,
c>0, c<200,
d>0, d<200,
e>0, e<200,
f>0, f<200,
(e+f)**2 + d**2 == 119**2,
(e+f)**2 + c**2 == 70**2,
e**2 + 30**2 == a**2,
f**2 + 30**2 == b**2,
a*d == 119*30,
b*c == 70*30,
a*f - 119*e + a*e == 0,
b*e - 70*f + b*f == 0,
d*e == c*f)
Unfortunately, z3 reports...
$ python laddersZ3.py
failed to solve
The problem does have at least this integer solution: a=34, b=50, c=42, d=105, e=16, f=40.
Am I doing something wrong, or is this kind of system of equations / range constraints beyond what z3 can solve?
Thanks in advance for any help.
UPDATE, 5 years later: Z3 now solves this out of the box.
You can solve this using Z3 if you encode the integers as reals, which will force Z3 to use the nonlinear real arithmetic solver. See this for more details on the nonlinear integer vs. real arithmetic solvers: How does Z3 handle non-linear integer arithmetic?
Here's your example encoded as reals with the solution (z3py link: http://rise4fun.com/Z3Py/1lxH ):
a,b,c,d,e,f = Reals('a b c d e f')
solve(
a>0, a<200,
b>0, b<200,
c>0, c<200,
d>0, d<200,
e>0, e<200,
f>0, f<200,
(e+f)**2 + d**2 == 119**2,
(e+f)**2 + c**2 == 70**2,
e**2 + 30**2 == a**2,
f**2 + 30**2 == b**2,
a*d == 119*30,
b*c == 70*30,
a*f - 119*e + a*e == 0,
b*e - 70*f + b*f == 0,
d*e == c*f) # yields [a = 34, b = 50, c = 42, d = 105, e = 16, f = 40]
While the result is integer as you noted, and as what Z3 finds, Z3 apparently needs to use the real arithmetic solver to handle it.
Alternatively, you can leave the variables declared as integers and do the following from the suggestion at the referenced post:
t = Then('purify-arith','nlsat')
s = t.solver()
solve_using(s, P)
where P is the conjunction of the constraints (z3py link: http://rise4fun.com/Z3Py/7nqN ).
Rather than asking Z3 for a solution in reals, you could ask the solver of the Microsoft Solver Foundation:
using Microsoft.SolverFoundation.Services;
static Term sqr(Term t)
{
return t * t;
}
static void Main(string[] args)
{
SolverContext context = SolverContext.GetContext();
Domain range = Domain.IntegerRange(1, 199); // integers ]0; 200[
Decision a = new Decision(range, "a");
Decision b = new Decision(range, "b");
Decision c = new Decision(range, "c");
Decision d = new Decision(range, "d");
Decision e = new Decision(range, "e");
Decision f = new Decision(range, "f");
Model model = context.CreateModel();
model.AddDecisions(a, b, c, d, e, f);
model.AddConstraints("limits",
sqr(e+f) + d*d == 119*119,
sqr(e+f) + c*c == 70*70,
e*e + 30*30 == a*a,
f*f + 30*30 == b*b,
a*d == 119*30,
b*c == 70*30,
a*f - 119*e + a*e == 0,
b*e - 70*f + b*f == 0,
d*e == c*f);
Solution solution = context.Solve();
Report report = solution.GetReport();
Console.WriteLine("a={0} b={1} c={2} d={3} e={4} f={5}", a, b, c, d, e, f);
Console.Write("{0}", report);
}
The solver comes up with the solution you mentioned within fractions of a second. The Express Edition used to be free, but I am not sure about the current state.
a: 34
b: 50
c: 42
d: 105
e: 16
f: 40
There is no algorithm that, in general, can answer whether a multivariate polynomial equation (or a system thereof, as in your case) has integer solution (this is the negative answer to Hilbert's tenth problem). Thus, all solving methods for integers are either restricted to certain classes (e.g. linear equations, polynomials in one variable...) or use incomplete tricks, such as:
Linearizing expressions
Encoding equations into finite-bitwidth
numbers (ok for searching for "small" solutions).
This is why Z3 needs to be told to use the real number solver.
I am trying to understand how the bound variables are indexed in z3.
Here in a snippet in z3py and the corresponding output. ( http://rise4fun.com/Z3Py/plVw1 )
x, y = Ints('x y')
f1 = ForAll(x, And(x == 0, Exists(y, x == y)))
f2 = ForAll(x, Exists(y, And(x == 0, x == y)))
print f1.body()
print f2.body()
Output:
ν0 = 0 ∧ (∃y : ν1 = y)
y : ν1 = 0 ∧ ν1 = y
In f1, why is the same bound variable x has different index.(0 and 1). If I modify the f1 and bring out the Exists, then x has the same index(0).
Reason I want to understand the indexing mechanism:
I have a FOL formula represented in a DSL in scala that I want to send to z3. Now ScalaZ3 has a mkBound api for creating bound variables that takes index and sort as arguments. I am not sure what value should I pass to the index argument. So, I would like to know the following:
If I have two formulas phi1 and phi2 with maximum bound variable indexes n1 and n2, what would be the index of x in ForAll(x, And(phi1, phi2))
Also, is there a way to show all the variables in an indexed form? f1.body() just shows me x in indexed form and not y. (I think the reason is that y is still bound in f1.body())
Z3 encodes bound variables using de Bruijn indices.
The following wikipedia article describes de Bruijn indices in detail:
http://en.wikipedia.org/wiki/De_Bruijn_index
Remark: in the article above the indices start at 1, in Z3, they start at 0.
Regarding your second question, you can change the Z3 pretty printer.
The Z3 distribution contains the source code of the Python API. The pretty printer is implemented in the file python\z3printer.py.
You just need to replace the method:
def pp_var(self, a, d, xs):
idx = z3.get_var_index(a)
sz = len(xs)
if idx >= sz:
return seq1('Var', (to_format(idx),))
else:
return to_format(xs[sz - idx - 1])
with
def pp_var(self, a, d, xs):
idx = z3.get_var_index(a)
return seq1('Var', (to_format(idx),))
If you want to redefine the HTML pretty printer, you should also replace.
def pp_var(self, a, d, xs):
idx = z3.get_var_index(a)
sz = len(xs)
if idx >= sz:
# 957 is the greek letter nu
return to_format('ν<sub>%s</sub>' % idx, 1)
else:
return to_format(xs[sz - idx - 1])
with
def pp_var(self, a, d, xs):
idx = z3.get_var_index(a)
return to_format('ν<sub>%s</sub>' % idx, 1)