Given non-negative real number tSS, tLS, tIS, tBS.
(i.e. they are real type with tSS>=0 and tLS>=0 and tIS>=0 and tBS>=0 and tSS>=0)
The following contraint C1 is in CNF format that contains 12 conjuncts.
(tSS+tLS<=tIS)And(tIS<=tBS)And(tSS+tLS+tIS+tBS<=3) OR
(tSS+tLS<=tBS)And(tBS<=tIS)And(tSS+tLS+tIS+tBS<=3) OR
(tSS+tLS<=tBS)And(tSS<=tIS)And(tIS<=tSS+tLS)And(tSS+tLS+tIS+tBS<=3) OR
(tSS<=tIS)And(tIS<=tBS)And(tBS<=tSS+tLS)And(tSS+tLS+tIS+tBS<=3) OR
(tSS+tLS<=tIS)And(tSS<=tBS)And(tBS<=tSS+tLS)And(tSS+tLS+tIS+tBS<=3) OR
(tSS<=tBS)And(tBS<=tIS)And(tIS<=tSS+tLS)And(tSS+tLS+tIS+tBS<=3) OR
(tSS+tLS<=tBS)And(tIS<=tSS)And(tSS+tLS+tIS+tBS<=3) OR
(tSS<=tBS)And(tIS<=tBS)And(tBS<=tSS+tLS)And(tSS+tLS+tIS+tBS<=3) OR
(tIS<=tBS)And(tBS<=tSS)And(tSS+tLS+tIS+tBS<=3) OR
(tSS+tLS<=tIS)And(tBS<=tSS)And(tSS+tLS+tIS+tBS<=3) OR
(tSS<=tIS)And(tBS<=tSS)And(tIS<=tSS+tLS)And(tSS+tLS+tIS+tBS<=3) OR
(tIS<=tSS)And(tBS<=tIS)And(tSS+tLS+tIS+tBS<=3)
I wish to obtain the constraint C2 in the form of
tSS<=a and tLS<=b and tIS<=c and tBS <=d and tSS<=e
The constraint C2 only needs to be included in C1, i.e.
any valuation satisfies C2 must satisfies C1, but not vice versa.
The value of a-e, is value that ranges from 0 to infinity.
Infinity means that it can take any value larger than 0.
Is it possible to use Z3 to infer the value of a-e? (it can be unsatisfiable)
There may be more efficient techniques to do it but at least you can solve the problem using quantifiers.
Let C1(tSS, tLS, tIS, tBS) denote the CNF formula and C2(SS, tLS, tIS, tBS, a, b, c, d) is the constraint to satisfy. You can check for satisfiability of the following quantified formula:
forall tSS tLS tIS tBS. C2(SS, tLS, tIS, tBS, a, b, c, d) => C1(tSS, tLS, tIS, tBS)
where a, b, c, d are free variables.
I encoded your concrete example using Z3 SMT online. The query is unsatisfiable in this case.
Related
How many equivalence classes in the RL relation for
{w in {a, b}* | (#a(w) mod m) = ((#b(w)+1) mod m)}
I am looking at a past test question which gives me the options
m(m+1)
2m
m^2
m^2+1
infinite
However, i claim that its m, and I came up with an automaton that I believe accepts this language which contains 3 states (for m=3).
Am I right?
Actually you're right. To see this, observe that the difference of #a(w) and #b(w), #a(w) - #b(w) modulo m, is all that matters; and there are only m possible values of this difference modulo m. So, m states are always sufficient to accept a language of this form: simply make the state corresponding to the appropriate difference the accepting state.
In your DFA, a2 corresponds to a difference of zero, a1 to a difference of one and a3 to a difference of two.
I have a Boolean formula (format: CNF) whose satisfiablity I check using the Z3 SAT solver. I am interested in obtaining partial assignments when the formula is satisfiable. I tried model.partial=true on a simple formula for an OR gate and did not get any partial assignment.
Can you suggest how this can be done? I have no constraints on the assignment other than that it be partial.
Z3's partial model mode are just for models of functions. For propositional formulas there is no assurance that models use a minimal number of assignments. On the contrary, the default mode of SAT solvers is that they find complete assignments.
Suppose you are interested in a minimized number of literals, such that the conjunction of the literals imply the formula. You can use unsat cores to get such subsets. The idea is that you first find a model of your formula F as a conjunction of literals l1, l2, ..., ln. Then given that this is a model of F we have that l1 & l2 & ... & ln & not F is unsatisfiable.
So the idea is to assert "not F" and check satisfiability of "not F" modulo assumptions l1, l2, .., ln. Since the result is unsat, you can query Z3 to retrieve an unsat core among l1, l2, .., ln.
From python what you would do is to create two solver objects:
s1 = Solver()
s2 = Solver()
Then you add F, respectively, Not(F):
s1.add(F)
s2.add(Not(F))
then you find a reduced model for F using both solvers:
is_Sat = s1.check()
if is_Sat != sat:
# do something else, return
m = s1.model()
literals = [sign(m, m[idx]()) for idx in range(len(m)) ]
is_sat = s2.check(literals)
if is_Sat != unsat:
# should not happen
core = s2.unsat_core()
print core
where
def sign(m, c):
val = m.eval(c)
if is_true(val):
return c
else if is_false(val):
return Not(c)
else:
# should not happen for propositional variables.
return BoolVal(True)
There are of course other ways to get reduced set of literals. A partial cheap way is to eagerly evaluate each clause and add literals from the model until each clause is satisfied by at least one literal in the model. In other words, you are looking for a minimal hitting set. You would have to implement this outside of Z3.
Lets say we have an expression in prefix notation or(1) and A B or(2) or(3) C D E (where A, B, C, D, E are boolean values and or numbered for convenience) that we want to convert to an infix notation. In principle I have two ways to evaluate it:
(1) start at or(3) C D, then or(2), then and, then or(1)
(2) start at and A B then check or(3), or(2). Lastly check or(1)
(1) Evaluate starting from right most operator
(2) Evaluate starting from left most operator having all operands as it's direct neighbors.
Both evaluations yield (A and B) or C or D or E.
Which evaluation sequence is correct?
Will these two evaluations ever give different result for the same prefix record?
http://www.cs.man.ac.uk/~pjj/cs212/fix.html recommends the first method.
You will get the same result regarding of the order, so it is up to you.
I want to create an expression that selects one of a given set of expressions. Given an array of expressions
Expr[] availableExprs = ...;
with statically known length, I want Z3 to select any one of these (like a switch statement). In case the problem is SAT I need a way to find out which of these was selected in the model (its index in the array).
What is the fastest way to encode this?
I considered these approaches so far:
Have an integer restricted to [0, arrayLength) and use ITE to select one of those expressions. The model allows me to extract this integer. Unfortunately, this introduces the integer theory to the model (which previously did not use integers at all).
Have one boolean for each possible choice. Use ITE to select an expression. Assert that exactly one of those booleans is true. This strategy does not need any special theory (I think) but the encoding might be too verbose.
Store the expressions into an array expression and read from that array using an integer. This saves the ITE chain but introduces the array theory.
Clearly, all of these work, but they all seem to have drawbacks. What is the best strategy?
If all you want is to encode is that an element v is in a finite set {e1, ..., en} (with sort U), you can do this using smtlib2 as follows:
(declare-fun v () U)
(declare-fun p1 () Bool)
...
(assert (= p1 (= v e1)))
(assert (= p2 (= v e2)))
...
(assert (= pn (= v en)))
(assert (or p1 ... pn))
The variable v will be equal to one of the elements in "the array" of {e1 ... en}. And pi must be true if the selection variable v is equal to ei. This is basically a restatement of Nikolaj's suggestion, but recast for arbitrary sorts.
Note that multiple pi may be set to true as there is no guarantee ei != ej. If you need to ensure no two elements are both selected, you will need to figure out what semantics you want. If the {e1... en} are already entailed to be distinct, you don't need to add anything. If the "array" elements must be distinct but are not yet entailed to be distinct, you can assert
(assert (distinct e1 ... en))
(This will probably be internally expanded to something quadratic in n.)
You can instead say that no 2 p variables can be true as once. Note that this is a weaker statement. To see this, suppose v = e1 = 1 and e2 = e3 = 0. Then p1 = true and p2 = p3 = false. The obvious encoding for these constraints is quadratic:
(assert (or (not pi) (not pj))) ; for all i < j
If you need a better encoding for this, try taking a look at how to encode "p1+ ... + pn <= 1" in Translating Pseudo-Boolean Constraints into SAT section 5.3. I would only try this if the obvious encoding fails though.
I think you want to make sure that each expression is quantifier free and uses only functions and predicates already present in the formula. If this is not the case then introduce a fresh propositional variable p_i for each index and assert ctx.MkIff(p_i, availableExprs[i]) to the solver.
When Z3 produces a model, use model.Eval(p_i) and check if the result is the expression "True".
I have a regression related question, but I am not sure how to proceed. Consider the following dataset, with A, B, C, and D as the attributes (features) and a decision variable Dec for each row:
A B C D Dec
a1 b1 c1 d1 Y
a1 b2 c2 d2 N
a2 b2 c3 d2 N
a2 b1 c3 d1 N
a1 b3 c2 d3 Y
a1 b1 c1 d2 N
a1 b1 c4 d1 Y
Given such data, I want to figure out most compact rules for which Dec evaluates to Y.
For example, A=a1 AND B=b1 AND D=d1 => Y.
I would prefer specifying the thresholds for the Precision of these rules, so that I can filter them out as per my requirement. For example, I would like to see all rules which provide at least 90% precision. This can provide me better compaction of the rules. The above mentioned rule provides 100% precision, whereas B=b1 AND D=d1 => Y has 66% precision (it errs on the 4th row).
Vaguely, I can see that this is similar to building a decision tree and finding out paths which end in Y. If I understand correctly, building a regression model would provide me the attributes which matter the most, but I need combinations of actual values from the attributes which lead to Y.
The attribute values are multi-valued, but that is not a hard constraint. I can even assume them to be boolean.
Is there any library in existing tools such as Weka or R that can help me?
Regards
I don't think this is a regression problem. This seems like a classification problem where you are trying to classify Y or N. You could build ensemble learners like Adaboost and see how the decisions vary from tree to tree or you could do something like elastic net logistic regression and see what the final weights are.