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Command line tool for SAT solving, SMT solving in various theories (NRA, LRA, LIA, EQ, EQUF, BV)

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Satisfiability checking

Command line tool for SAT solving, SMT solving in various theories, and other algorithms related to satisfiability checking.

The techniques used here are introduced in the RWTH University lecture 'Satisfiability checking' by Prof. Dr. Erika Ábrahám.

How to build and run the project

Clone the git repository:
git clone https://github.com/paultristanwagner/satisfiability-checking.git
Navigate into the created directory:
cd satisfiability-checking
Let Maven build the project:
mvn package
Run the project:
java -jar target/satchecking-1.0-SNAPSHOT.jar
Now you should see the command prompt indicated by a > symbol.

SMT solver

An SMT solver is implemented for non-linear real arithmetic (QF_NRA), linear real arithmetic (QF_LRA), linear integer arithmetic (QF_LIA), equality logic (QF_EQ), equality logic with uninterpreted functions (QF_EQUF) and bit vector arithmetic (QF_BV).

Examples

QF_NRA: Non-linear real arithmetic with optimization

> smt QF_NRA (y<=x^3) & (y<=-x^2+1) & (max(x+y))
SAT:
x=(x^3+x^2-1, 3/4, 7/8) ≈ 0.7548776662466928; y=(-1x^3+2x^2-3x+1, -4, 4) ≈ 0.4301597090019467;
Time: 297ms
> smt QF_NRA (x^2+y^2<1) & (min(y))
UNSAT
Time: 12ms
> smt QF_NRA (x^2 + y^2 = 1) & (x^2 + y^3 = 1/2)
SAT:
x=(x^6-2x^4+2x^2-3/4, 3/4, 15/16) ≈ 0.8249554777467228; y=(x^9+1/2x^6+3/4x^3+1/8, -7/4, 7/4) ≈ -0.5651977173836394;
Time: 456ms
> smt QF_NRA (x*y > 0) & (y*z > 0) & (x*z > 0) & (x + y + z = 0)
UNSAT
Time: 7ms

QF_LRA: Linear real arithmetic with optimization

> smt QF_LRA (x<=-3 | x>=3) & (y=5) & (x+y>=12) & (min(x))
SAT:
x=7; y=5;
Time: 0ms
> smt QF_LRA (x<=0 | x>=5) & (x+y=5/2) & (y=1)
UNSAT
Time: 1ms

QF_LIA: Linear integer arithmetic with optimization

> smt QF_LIA (y+0.8x<=4) & (y-0.25x>=0) & (max(x))
SAT:
x=3; y=1;
Time: 1ms
> smt QF_LIA (y-x<=0) & (y+x<=1) & (y>=0.1)
UNSAT
Time: 1ms

QF_EQ: Equality logic

> smt QF_EQ (a=b) & (c=d) & (a!=d)
SAT:
a=0.0; b=0.0; c=1.0; d=1.0;
Time: 0ms
> smt QF_EQ (a=b) & (b=c) & (c=d) & (a!=d)
UNSAT
Time: 0ms

QF_EQUF: Equality logic with uninterpreted functions

> smt QF_EQUF (x1 = x2) & (x2 = x3) & (x4 = x5) & (f(x1) != f(x5))
SAT:
f(x1)=0.0; f(x5)=3.0; x1=2.0; x2=2.0; x3=2.0; x4=1.0; x5=1.0;
Time: 0ms
> smt QF_EQUF (f(f(y)) != x) & (x = f(y)) & (y = u) & (x = y)
UNSAT
Time: 0ms

QF_BV: Bit vector arithmetic

> smt QF_BV (x >= 0) & (y > 0) & (y[6]) & (x >> y = 0) & (x + y < x * y)
SAT:
x=0b01010101 (85); y=0b01000000 (64);
Time: 3ms
> smt QF_BV (a * b = c) & (b * a = c) & (x < y) & (y < x)
UNSAT
Time: 11ms

Propositional logic

A SAT solver is implemented that can employ DPLL+CDCL, DPLL as well as simple enumeration to solve propositional logic problems.

Input can be given in conjunctive normal form in the following way.

> sat (a) & (~a | b) & (~b | ~c)
SAT:
a=1, b=1, c=0;
1 model/s found in 0 ms
> sat (a) & (~a | b) & (~b)
UNSAT

Tseitin's transformation

The Tseitin transformation is implemented for propositional logic. It can be used to transform a formula into an equi-satisfiable formula in conjunctive normal form. The logical operators '~', '&', '|', '->', '<->' as well as parentheses are supported.

> tseitin ~(a & (a -> b) -> b)
Tseitin's transformation:
(~h3 | ~a | b) & (a | h3) & (~b | h3) & (~h2 | a) & (~h2 | h3) & (~a | ~h3 | h2) & (~h1 | ~h2 | b) & (h2 | h1) & (~b | h1) & (~h0 | ~h1) & (h1 | h0) & (h0)

Theory solvers

Linear real arithmetic (QF_LRA)

The program can check sets of weak linear constraints for satisfiability employing the Simplex algorithm.
If the set of constraints is satisfiable, the program will print a satisfying assignment. Otherwise, an explanation for unsatisfiability is given in the form of an infeasible subset. Decimals, as well as fractions are supported which will be handled by exact rational arithmetic. This setting can be changed to use floating point arithmetic in the config.properties file.

Examples

> simplex a+3b+5c=30 a>=5 a<=10 b>=2 c>=1
SAT!
Solution: a=10; b=5; c=1; Time: 0ms
> simplex x+y=3 y=1 x<=1
UNSAT!
Explanation: x<=1; y=1; x+y=3; Time: 0ms

An optional objective function can be given to maximize or minimize the value of a linear expression.

> simplex min(-2x-3y-4z) 3x+2y+z<=10 2x+5y+3z<=15 x>=0 y>=0 z>=0
SAT! (optimal)
Solution: x=0; y=0; z=5; Optimum: -20
Time: 0ms
> simplex max(x) x>=-1 x>=-1/2
UNSAT! (feasible, but unbounded)
Solution: x=-1/2;
Time: 0ms