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gophersat's Issues

--count does not work

when I run with --count it gives me the same output as without --count.
I checked in the main function and it does not take this parameter into consideration.

would be good to put these test files into the repo instead of living in some home dir

getting errors when running go test

solver_test.go:17: open /Users/delorme/prog/sat/25.cnf: no such file or directory

stens@cameroon ~/src/gopath/src/github.com/crillab/gophersat/solver $ go test
c ======================================================================================
c ======================================================================================
c ======================================================================================
c ======================================================================================
c ======================================================================================
--- FAIL: TestSolver (0.00s)
solver_test.go:17: open /Users/delorme/prog/sat/25.cnf: no such file or directory
solver_test.go:17: open /Users/delorme/prog/sat/50.cnf: no such file or directory
solver_test.go:17: open /Users/delorme/prog/sat/75.cnf: no such file or directory
solver_test.go:17: open /Users/delorme/prog/sat/100.cnf: no such file or directory
solver_test.go:17: open /Users/delorme/prog/sat/125.cnf: no such file or directory
solver_test.go:26: Invalid result for "/Users/delorme/prog/sat/125.cnf": expected 2, got 1
c ======================================================================================
c ======================================================================================
c | Restarts | Conflicts | Learned | Deleted | Del% | Reduce | Units learned |
c | Restarts | Conflicts | Learned | Deleted | Del% | Reduce | Units learned |
c ======================================================================================
c ======================================================================================
FAIL
c ======================================================================================
exit status 1
FAIL github.com/crillab/gophersat/solver 0.001s

`solver.Exactly1` returns incorrect `AtLeast1` constraint.

First of all, thank you for this delightful library :)

Just a small problem with the solver.Exactly1 method.

To reproduce:

fmt.Printf("%+v\n", solver.Exactly1(1, 2, 3))
// expected output: [{Lits:[1 2 3] AtLeast:1} {Lits:[-1 -2 -3] AtLeast:2}]
// actual output: [{Lits:[-1 -2 -3] AtLeast:1} {Lits:[-1 -2 -3] AtLeast:2}]

bf.Solve() panics with some inputs

func TestPanic1(t *testing.T) {
	bf.Solve(bf.And(bf.Var("x"), bf.Var("x")))
}

leads to

panic: runtime error: makeslice: cap out of range

Also this:

func TestPanic2(t *testing.T) {
	bf.Solve(bf.And(bf.Var("x"), bf.And(bf.Var("x"), bf.Var("y"))))
}

Is this expected ?

[Problem] a satisfiable formula has also a MUS. It should not be possible.

Hello GopherSAT team.

I found the symmetric of the previous bug reported in Issue #5.
I have now a satisfiable formula where the MUS() query does not answer an empty set.
Again, you can download the formula here: Download here.

That being said, here are the logs of my claim that the formula is indeed satisfiable and thus that the MUS should be empty.

gophersat -verbose impossible.cnf
c solving impossible.cnf
c ======================================================================================
c | Number of non-unit clauses :     11117                                             |
c | Number of variables        :      8924                                             |
c ======================================================================================
c | Restarts |  Conflicts  |  Learned  |  Deleted  | Del% | Reduce |   Units learned   |
c ======================================================================================
c ======================================================================================
s SATISFIABLE
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c nb conflicts: 1
c nb restarts: 0
c nb decisions: 9116
c nb unit learned: 0
c nb binary learned: 1
c nb learned: 1
c nb learned clauses deleted: 0

And when I try to obtain a MUS from this formula, here is the log:

gophersat -mus -verbose impossible.cnf

p cnf 8924 1802
91 0
10 0
11 0
[ ... ]
71 0
-72 0
98 0
80 0
81 0
117 0
90 0
-1634 0
[ ... ]
2604 -145 -146 0

Obviously, a satisfiable formula cannot have an unsatisfiable subset, by definition.
As in Issue #5, here are some logs from state-of-the-art SAT solvers:

Glucose 4.2.1 (2019):

#### glucose-4.2.1:2019 ####
+ cd /solvers/glucose-4.2.1
+ ./glucose /data/impossible.cnf -model
c
c This is glucose 4.2.1 --  based on MiniSAT (Many thanks to MiniSAT team)
c
c ========================================[ Problem Statistics ]===========================================
c |                                                                                                       |
c |  Number of variables:          8924                                                                   |
c |  Number of clauses:           11117                                                                   |
c |  Parse time:                   0.00 s                                                                 |
c |                                                                                                       |
c | Preprocesing is fully done
c |  Eliminated clauses:           0.06 Mb                                                                |
c |  Simplification time:          0.01 s                                                                 |
c |                                                                                                       |
c ========================================[ MAGIC CONSTANTS ]==============================================
c | Constants are supposed to work well together :-)                                                      |
c | however, if you find better choices, please let us known...                                           |
c |-------------------------------------------------------------------------------------------------------|
c | Adapt dynamically the solver after 100000 conflicts (restarts, reduction strategies...)               |
c |-------------------------------------------------------------------------------------------------------|
c |                                |                                |                                     |
c | - Restarts:                    | - Reduce Clause DB:            | - Minimize Asserting:               |
c |   * LBD Queue    :     50      |   * First     :   2000         |    * size <  30                     |
c |   * Trail  Queue :   5000      |   * Inc       :    300         |    * lbd  <   6                     |
c |   * K            :   0.80      |   * Special   :   1000         |                                     |
c |   * R            :   1.40      |   * Protected :  (lbd)< 30     |                                     |
c |                                |                                |                                     |
c ==================================[ Search Statistics (every  10000 conflicts) ]=========================
c |                                                                                                       |
c |          RESTARTS           |          ORIGINAL         |              LEARNT              | Progress |
c |       NB   Blocked  Avg Cfc |    Vars  Clauses Literals |   Red   Learnts    LBD2  Removed |          |
c =========================================================================================================
c last restart ## conflicts  :  1 404
c =========================================================================================================
c restarts              : 1 (1 conflicts in avg)
c blocked restarts      : 0 (multiple: 0)
c last block at restart : 0
c nb ReduceDB           : 0
c nb removed Clauses    : 0
c average learnt size   : 2
c nb learnts DL2        : 1
c nb learnts size 2     : 1
c nb learnts size 1     : 0
c conflicts             : 1              (111 /sec)
c decisions             : 628            (0.00 % random) (69523 /sec)
c propagations          : 1979           (219086 /sec)
c nb reduced Clauses    : 0
c LCM                   : 0 / 0
c CPU time              : 0.009033 s

s SATISFIABLE
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CaDiCal 2019:

#### cadical:2019 ####
c --- [ banner ] -------------------------------------------------------------
c 
c CaDiCaL Radically Simplified CDCL SAT Solver
c Copyright (c) 2016-2019 Armin Biere, JKU Linz
c 
c Version sr2019 9362f4982cb613599837677566b6c35c7ac9175e
c g++ (Debian 8.3.0-6) 8.3.0 -Wall -Wextra -O3 -DNDEBUG
c Sat Mar 7 17:16:00 UTC 2020 Linux abe47dad2b3d 5.0.0-37-generic x86_64
c 
c --- [ config ] -------------------------------------------------------------
c 
c using 'default' configuration (should work in most situations)
c 
c --- [ parsing input ] ------------------------------------------------------
c 
c reading DIMACS file from '/data/impossible.cnf'
c opening file to read '/data/impossible.cnf'
c found 'p cnf 8924 12908' header
c parsed 12908 clauses in 0.00 seconds process time
c 
c --- [ options ] ------------------------------------------------------------
c 
c all options are set to their default value
c 
c --- [ solving ] ------------------------------------------------------------
c 
c time measured in process time since initialization
c 
c  seconds  reductions  redundant  irredundant
c         MB    restarts       trail     variables
c          level   conflicts       glue      remaining
c 
c *  0.00  5  0 0   0    0    0  0% 0 11117 815  9%
c l  0.01  6  0 0   0    0    0  0% 0 11117 815  9%
c 1  0.01  6  0 0   0    0    0  0% 0 11117 815  9%
c 
c --- [ result ] -------------------------------------------------------------
c 
s SATISFIABLE
v 1 2 3 4 5 6 7 8 9 10 11 -12 13 -14 -15 -16 -17 -18 19 20 21 -22 23 -24 -25 [ ... ]
c 
c --- [ run-time profiling ] -------------------------------------------------
c 
c process time taken by individual solving procedures
c (percentage relative to process time for solving)
c 
c         0.00   69.39% search
c         0.00   69.19% lucky
c         0.00    0.00% simplify
c   =================================
c         0.00   16.42% solve
c 
c last line shows process time for solving
c (percentage relative to total process time)
c 
c --- [ statistics ] ---------------------------------------------------------
c 
c fixed:                      1071        12.00 %  of all variables
c lucky:                         1       100.00 %  of tried
c propagations:               1071         1.04 M  per second
c 
c seconds are measured in process time for solving
c 
c --- [ resources ] ----------------------------------------------------------
c 
c total process time since initialization:         0.01    seconds
c total real time since initialization:            0.01    seconds
c maximum resident set size of process:            5.50    MB
c 
c --- [ shutting down ] ------------------------------------------------------
c 
c exit 10

MiniSAT 2.2.0:

#### minisat-v2.2.0-106-ge2dd095:2019 ####
c WARNING: for repeatability, setting FPU to use double precision
c ============================[ Problem Statistics ]=============================
c |                                                                             |
c |  Number of variables:          8924                                         |
c |  Number of clauses:           11117                                         |
c |  Parse time:                   0.00 s                                       |
c |  Eliminated clauses:           0.06 Mb                                      |
c |  Simplification time:          0.01 s                                       |
c |                                                                             |
c ============================[ Search Statistics ]==============================
c | Conflicts |          ORIGINAL         |          LEARNT          | Progress |
c |           |    Vars  Clauses Literals |    Limit  Clauses Lit/Cl |          |
c ===============================================================================
c ===============================================================================
c restarts              : 1
c conflicts             : 1              (85 /sec)
c decisions             : 627            (0.00 % random) (53425 /sec)
c propagations          : 1978           (168541 /sec)
c conflict literals     : 2              (0.00 % deleted)
c Memory used           : 8.48 MB
c CPU time              : 0.011736 s
c 
s SATISFIABLE
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The models seem to match a real solution to my input problem (even though I did not fully verify them with an SAT-checker).
Nevertheless, the only incorrect result here to the best of my knowledge is that the MUS answered by GopherSAT should be empty and is, therefore, incorrect.

Sover panics in special circumstances.

Hi,
I've seen an error in one case using maxsat (only with solftclauses). I nailed it down to a minimum set of cluauses causing the issue:

x := maxsat.Var("x")
hard := maxsat.HardClause(x)
soft := maxsat.SoftClause(x.Negation())

problem := maxsat.New(soft, hard)
problem.Solve()

The error output is:
=== RUN TestMaxSat
--- FAIL: TestMaxSat (0.00s)
panic: runtime error: index out of range [recovered]
panic: runtime error: index out of range

goroutine 18 [running]:
testing.tRunner.func1(0xc42015c0f0)
/usr/local/go/src/testing/testing.go:742 +0x29d
panic(0x571a60, 0x6879a0)
/usr/local/go/src/runtime/panic.go:502 +0x229
github.com/crillab/gophersat/solver.(*Solver).cleanupBindings(0xc420162000, 0x1)

The MUS extraction in Gophersat provides a wrong result

Hello GopherSAT team. Poke @fdelorme, @adam1, @9034725985 , @frrad

First of all, kudos to the good work of having such a user-friendly SAT solver!
However, I found a discrepancy in the results obtained using gophersat.
Here is an UNSATISFIABLE formula, downloadable here, unsatisfiable trivially by nature of what I am trying to solve.

Here is the log of the discrepancy obtained (I had to rename the file into a *.txt since *.cnf is not uploadable on Github).
When one tries to solve the formula using gophersat, one obtains the following:

gophersat -verbose -certified test.cnf
c solving test.cnf
c ======================================================================================
c | Number of non-unit clauses :       812                                             |
c | Number of variables        :     16347                                             |
s UNSATISFIABLE
c nb conflicts: 0
c nb restarts: 0
c nb decisions: 0
c nb unit learned: 0
c nb binary learned: 0
c nb learned: 0
c nb learned clauses deleted: 0

which is, to the best of my knowledge, the correct answer (see the correctness asserted by using other solvers below).
However, when one tries to extract a MUS thanks to gophersat, one obtains the following result:

gophersat -mus test.cnf
could not extract subset: could not extract MUS: problem is not UNSAT

which is inconsistent, a formula cannot be satisfiable and unsatisfiable at the same time.
Feel free to contact me at [email protected] if you want to discuss the original structure of this formula or/and if I can help you to find the source of the bug.


To confirm the fact that the formula is indeed UNSATISFIABLE, here are the answers provided by famous (and recent) SAT solvers.

CaDiCaL 2019:

+ cd /solvers/CaDiCaL
+ ./cadical --default /data/test.cnf
c --- [ banner ] -------------------------------------------------------------
c
c CaDiCaL Radically Simplified CDCL SAT Solver
c Copyright (c) 2016-2019 Armin Biere, JKU Linz
c
c Version sr2019 9362f4982cb613599837677566b6c35c7ac9175e
c g++ (Debian 8.3.0-6) 8.3.0 -Wall -Wextra -O3 -DNDEBUG
c Sat Mar 7 17:16:00 UTC 2020 Linux abe47dad2b3d 5.0.0-37-generic x86_64
c
c --- [ config ] -------------------------------------------------------------
c
c using 'default' configuration (should work in most situations)
c
c --- [ parsing input ] ------------------------------------------------------
c
c reading DIMACS file from '/data/test.cnf'
c opening file to read '/data/test.cnf'
c found 'p cnf 16347 812' header
c found falsified original clause
c parsed 812 clauses in 0.00 seconds process time
c
c --- [ options ] ------------------------------------------------------------
c
c all options are set to their default value
c
c --- [ solving ] ------------------------------------------------------------
c
c time measured in process time since initialization
c
c  seconds  reductions  redundant irredundant
c         MB    restarts       trail    variables
c          level   conflicts       glue     remaining
c
c 0  0.00  3  0 0   0    0    0  0% 0    0   0  0%
c
c --- [ result ] -------------------------------------------------------------
c
s UNSATISFIABLE
c
c --- [ run-time profiling ] -------------------------------------------------
c [...]

Glucose 4.2.1 (2019):

+ cd /solvers/glucose-4.2.1
+ ./glucose /data/test.cnf -model
c
c This is glucose 4.2.1 --  based on MiniSAT (Many thanks to MiniSAT team)
c
c ========================================[ Problem Statistics ]===========================================
c |                                                                                                       |
c |  Number of variables:         16347                                                                   |
c |  Number of clauses:               0                                                                   |
c |  Parse time:                   0.00 s                                                                 |
c |                                                                                                       |
c | Preprocesing is fully done
c |  Simplification time:          0.00 s                                                                 |
c |                                                                                                       |
c =========================================================================================================
Solved by simplification
c restarts              : 0 (0 conflicts in avg)
c blocked restarts      : 0 (multiple: 0)
c last block at restart : 0
c nb ReduceDB           : 0
c nb removed Clauses    : 0
c average learnt size   : 0
c nb learnts DL2        : 0
c nb learnts size 2     : 0
c nb learnts size 1     : 0
c conflicts             : 0              (0 /sec)
c decisions             : 0              (-nan % random) (0 /sec)
c propagations          : 246            (231421 /sec)
c nb reduced Clauses    : 0
c LCM                   : 0 / 0
c CPU time              : 0.001063 s

s UNSATISFIABLE

MiniSAT 2.2.0

+ cd /solvers/Minisat-v2.2.0-106-ge2dd095
+ ./minisat /data/test.cnf
c WARNING: for repeatability, setting FPU to use double precision
c ============================[ Problem Statistics ]=============================
c |                                                                             |
c |  Number of variables:         16347                                         |
c |  Number of clauses:               0                                         |
c |  Parse time:                   0.00 s                                       |
c |  Simplification time:          0.00 s                                       |
c |                                                                             |
c ===============================================================================
c Solved by simplification
c restarts              : 0
c conflicts             : 0              (0 /sec)
c decisions             : 0              (-nan % random) (0 /sec)
c propagations          : 246            (140571 /sec)
c conflict literals     : 0              (-nan % deleted)
c Memory used           : 8.99 MB
c CPU time              : 0.00175 s
c
s UNSATISFIABLE

SAT4J 2013 (Competition)

+ cd /solvers/SAT4J
+ java -Xms5g -Xmx5g -jar .//sat4j2013.jar /data/test.cnf
c SAT4J: a SATisfiability library for Java (c) 2004-2013 Artois University and CNRS
c This is free software under the dual EPL/GNU LGPL licenses.
c See www.sat4j.org for details.
c version SAT COMPETITION 2013
c java.runtime.name     OpenJDK Runtime Environment
c java.vm.name          OpenJDK 64-Bit Server VM
c java.vm.version       25.242-b08
c java.vm.vendor        Oracle Corporation
c sun.arch.data.model   64
c java.version          1.8.0_242
c os.name               Linux
c os.version            4.19.76-linuxkit
c os.arch               amd64
c Free memory           5091654344
c Max memory            5145362432
c Total memory          5145362432
c Number of processors  2
c --- Begin Solver configuration ---
c org.sat4j.minisat.constraints.MixedDataStructureDanielWL@7cd84586
c Learn all clauses as in MiniSAT
c claDecay=0.999 varDecay=0.95 conflictBoundIncFactor=1.5 initConflictBound=100
c VSIDS like heuristics from MiniSAT using a heap lightweight component caching from RSAT
c Expensive reason simplification
c Glucose 2.1 dynamic restart strategy
c Glucose 2 learned constraints deletion strategy
c timeout=2147483647s
c DB Simplification allowed=true
c Listener: org.sat4j.minisat.core.VoidTracing@1b2c6ec2
c --- End Solver configuration ---
c solving /data/test.cnf
c reading problem ...
c (trivial inconsistency)
c starts                : 0
c conflicts             : 0
c decisions             : 0
c propagations          : 0
c inspects              : 0
c shortcuts             : 0
c learnt literals       : 0
c learnt binary clauses : 0
c learnt ternary clauses        : 0
c learnt constraints    : 0
c ignored constraints   : 0
c root simplifications  : 0
c removed literals (reason simplification)      : 0
c reason swapping (by a shorter reason) : 0
c Calls to reduceDB     : 0
c Number of update (reduction) of LBD   : 0
c Imported unit clauses : 0
c speed (assignments/second)    : 0.0
c non guided choices    0
c learnt constraints type
s UNSATISFIABLE
c Total wall clock time (in seconds) : 0.015

undefined: solver.ParsePBConstrs

I might be missing something obvious, but a vanilla install fails for me:

➜  ~ go get github.com/crillab/gophersat
# github.com/crillab/gophersat/maxsat
go/src/github.com/crillab/gophersat/maxsat/problem.go:62:10: undefined: solver.ParsePBConstrs

Possibly related to 9b32be3

Incorrect number of models after creating problem with ParseSlice

Hey,
first of all I'm actually not that familiar with the field of SAT solving and solvers, therefore I hope that I don't waste your time.

I have a small assignment of converting the problem of solving a game of Unruly into a SAT instance. Which has all worked pretty nice and the models I get are all correct.

My problem rises when I want to get the number of possible solutions/models. I know for a fact that all the puzzles have exactly one solution and also double checked that using PicoSat 965. For this I created a valid CNF file. Using this file also yields the correct result when using gophersat, but as soon as I want to use ParseSlice in order to avoid creating the cnf string, writing it to a file and reading it immediately afterwards I get incorrect results.

I had a look at the code and realized that instead of calling simplify2 like ParseCNF does (when reading from a .cnf file) ParseSlice calls simplify. As far as I can tell simplify runs an UP just like simplify2. Therefore I changed the call in ParseSlice to simplify2 and voilà it works.

As I said I'm not that familiar with SAT solvers and am not sure wether this will break something else down the road.

Unfortunately I wasn't able to find a simple example to reproduce this behaviour and it only occurs when I test the biggest games provided (32x32 fields). Hence the resulting cnf file and slice of a slice of ints are pretty big. That's why I'm not posting these files yet..

Have a good one

Assume() should return SAT in the case of a Propagation-complete problem.

Hello GopherSAT team.

I have either found a bug or I need an explanation on why this is not the expected behaviour.

Here is a unit test generating my strange case:

//
// ***************************************************************************************************
// @date 10/09/2020.
// @author Valentin Montmirail
//
// Here we want to test the behaviour of selectors in the following case:
//
// SUM( -sel + a + b + c +d ) <= 1
// && (sel v -a)
// && (sel v -b)
// && (sel v -c)
// && (sel v -d)
//
// In such a problem, we have two cases for our assumption: either sel is true, or it is false.
//
// [SEL is assumed to be true].
//
// 		The set of clauses ((sel v -a) && (sel v -b) && (sel v -c) && (sel v -d)) can be removed.
// 		Since they are tautological under our assumption.
// 		The problem thus becomes: SUM( a + b + c + d) <= 1. 
// 		Which is INDETERMINATE until we decided one of the truth values
// 		of either 'a', 'b', 'c' or 'd'.
//
//
// [SEL is assumed to be false].
//
// 		Then in such case, the set of clauses ((sel v -a) && (sel v -b) && (sel v -c) && (sel v -d)) becomes
// 		a set of unit clauses: (-a), (-b), (-c) and (-d). So the truth values of a,b,c and d are assigned.
//		The other constraint,  [SUM( -sel + a + b + c +d ) <= 1] becomes tautological since (-sel) equals 1.
// 		In such case, just assuming that SEL is false should already solve the problem by Unit Propagation.
//
// ***************************************************************************************************
func TestAssumptionInCardinalityConstraint(t *testing.T) {

	// Constraint will store all the constraints of our toy problem.
	constraints := make([]solver.CardConstr, 0)

	// For now, the problem is assumed to be INDETERMINATE.
	status := solver.Indet

	// We have the following variables in our problem.
	a := 1
	b := 2
	c := 3
	d := 4
	sel := 5

	sum := make([]int, 0)
	sum = append(sum, -sel)
	sum = append(sum, a)
	sum = append(sum, b)
	sum = append(sum, c)
	sum = append(sum, d)

	// constraints is thus containing: SUM( -sel + a + b + c +d ) <= 1
	constraints = append(constraints, solver.AtMost1(sum...))

	// We add the other binary clauses
	// We add (sel v -a)
	constraints = append(constraints, solver.AtLeast1(sel, -a))

	// We add (sel v -b)
	constraints = append(constraints, solver.AtLeast1(sel, -b))

	// We add (sel v -c)
	constraints = append(constraints, solver.AtLeast1(sel, -c))

	// We add (sel v -d)
	constraints = append(constraints, solver.AtLeast1(sel, -d))

	// We create the problem as a SAT problem (so we convert the cardinality constraints into clauses)
	problem := solver.ParseCardConstrs(constraints)

	// We create our solver from this set of clauses.
	solv := solver.New(problem)

	// ---------------------------------------------------------------------------------------------------
	// First case:  [SEL is assumed to be true].

	selectors := make([]solver.Lit, 1)
	selectors = append(selectors, solver.IntToLit(int32(sel)))

	// We ask the solver to assume that sel is true.
	status = solv.Assume(selectors)

	// As expected, the status should be INDETERMINATE since we still need to find a value for a, b, c and d.
	if status != solver.Indet {
		t.Error("We still need to decide the ExactlyOne constraint.")
	}

	// Obviously if we solve the problem, 
	// the solver will find a truth value for each variable and the problem becomes satisfiable.
	status = solv.Solve()
	if status != solver.Sat {
		t.Error("The ExactlyOne constraint must be satisfiable.")
	}

	// we can check the truth values of each variables.
	model := solv.Model()

	if len(model) != 5 {
		t.Error("Model: ", model)
		t.Fatal("We have a problem, we do not have a truth value for each variable.")
	}

	truthA := model[0]
	truthB := model[1]
	truthC := model[2]
	truthD := model[3]
	truthSel := model[4]

	if truthSel != true {
		t.Error("We have a problem, we have assumed sel to be true, so it must be true in the final model.")
	}

	if truthA == true {
		if (truthB || truthC || truthD) != false {
			t.Error("We have two different variables in the SUM, assigned to true.")
		}
	} else if truthB == true {
		if (truthA || truthC || truthD) != false {
			t.Error("We have two different variables in the SUM, assigned to true.")
		}
	} else if truthC == true {
		if (truthA || truthB || truthD) != false {
			t.Error("We have two different variables in the SUM, assigned to true.")
		}
	} else if truthD == true {
		if (truthA || truthB || truthC) != false {
			t.Error("We have two different variables in the SUM, assigned to true.")
		}
	} else {
		t.Error("One of the four free variables must be SET to true.")
	}

	// ---------------------------------------------------------------------------------------------------
	// Second case:  [SEL is assumed to be false].

	selectors = nil
	selectors = append(selectors, solver.IntToLit(int32(-sel)))

	// We ask the solver to assume that sel is false.
	status = solv.Assume(selectors)

	// Technically, we should solve the problem by Unit propagation, so the return of Assume should be solver.Sat
	if status != solver.Sat {
		t.Error("The ExactlyOne constraint is already satisfied... The status is for now: ", status)
	}

	// As before, if we ask the solver to solve the problem, it should be satisfiable.
	status = solv.Solve()
	if status != solver.Sat {
		t.Error("The ExactlyOne constraint must be satisfiable.")
	}

	model = solv.Model()

	if len(model) != 5 {
		t.Error("Model: ", model)
		t.Fatal("We have a problem, we do not have a truth value for each variable.")
	}

	truthA = model[0]
	truthB = model[1]
	truthC = model[2]
	truthD = model[3]
	truthSel = model[4]

	if truthSel != false {
		t.Error("We have a problem, we have assumed sel to be false, so it must be true in the final model.")
	}

	if truthA != false {
		t.Error("We have assigned a to true. But we cannot since sel is already solving the AtMostOne()")
	}

	if truthB != false {
		t.Error("We have assigned b to true. But we cannot since sel is already solving the AtMostOne()")
	}

	if truthC != false {
		t.Error("We have assigned c to true. But we cannot since sel is already solving the AtMostOne()")
	}

	if truthD != false {
		t.Error("We have assigned d to true. But we cannot since sel is already solving the AtMostOne()")
	}

	// ---------------------------------------------------------------------------------------------------
}

In this test, everything is working as expected except for one case. When we assume the selector to be false, then the problem should be considered as satisfiable after the first Unit Propagation. Unfortunately, it is not the case and the function Assume() when the selector is false returns INDETERMINATE.

Is this normal behaviour or gopherSAT deals incorrectly with selectors in the Assume() function?

MUS still not correct

Hi I saw a few closed issues and merged MR regarding incorrect MUS functionality but I still find it returning incorrect MUS

For example can try this problem:

p cnf 10 11
-1 0
-2 0
-3 0
-4 0
-5 0
-6 0
-7 0
-8 0
-9 0
-10 0
1 3 5 7 0

with latest commit in master (0b6a35c) it returns this as MUS:

> gophersat -mus unsat.cnf
p cnf 10 10
-1 0
-2 0
-3 0
-4 0
-5 0
-6 0
-7 0
-8 0
-9 0
-10 0

which shouldn't be correct, that's not UNSAT. The correct one should be

p cnf 10 5
-1 0
-3 0
-5 0
-7 0
1 3 5 7 0

hello world sat

Hi,

I am interested in spinning the wheels with gophersat. Just curious, is there a standard 'hello world' type problem that only SAT easily solves? If so, can you point me to one and I will try to learn enough to convert to Go (as a driver) and solve with gophersat. Then will do a PR so we have an example here.

Thanks for the project.

Ron

LGPL?

Is there a particular reason this library is LGPL? Using LGPL in the context of Go is effectively the same as the GPL, which runs pretty contrary to the overal community of Go developers. Would whomever be open to relicensing this to something more permissive and common within the Go community, like either the MIT, BSD, or Apache licenses?

https://groups.google.com/forum/#!topic/golang-nuts/JqOAWBpL-70

Run ParseSlice on a CNF instance programmatically

Hello, good afternoon!

The README was super helpful when installing the library and using gophersat from the command line, but I could not find a way to properly import it as a library from another go program (surely because I'm new to go and I'm not familiar with go mod, go get, and the way package management should is done in go).

Although it is perfectly possible to circumvent this issue by writing CNF instances to a file and then using the built-in os package to invoke gophersat, I would like to know if there is a way to add gophersat as a usable dependency directly on a go program, instead of relying on os.exec or os.sys as intermediaries.

If I understood correctly, it should be possible to follow these steps in order to get a model using the solver:

  1. Call ParseSlice() on a dimacs cnf problem instance stored as [][]int, which would yield a *Problem,
  2. then call New() on that problem to get a solver as *Solver,
  3. call Solve(), and then
  4. go through the problem variables and check their truth value based on how truth values are encoded as declevel on the .model field of the solver (if the _n_th variable is true, then the _n_th position on the model slice is positive, and if the variable in the nth position is set to false, then the nth position is negative).

There's probably a more direct way, but that's what I could come up with after skimming the .go files on the repo.

I think users who plan to invoke the solver programmatically could benefit from having some brief guidelines available on the readme. If you think this could be useful and don't find it inconvenient, I could see if I can get some examples working smoothly and write a short tutorial explaining what each struct and function does, and how to go about carrying out some common tasks.

Have a nice day!

[Enhancement Proposal] Only one type of problem for Solve, Explain and MaxSAT

You have three structures Problem one in the packageexplain, one in the package maxsat and one in the package solver.

However, It seems that it could be convenient to first solve a problem and if the result is unsatisfiable, then find an explanation (MUS or just an unsatisfiable subset).

A simple (but inefficient) way is to do it as follows:

func Convert(problem *solver.Problem) (*explain.Problem, error) {

	reader := strings.NewReader(problem.CNF())
	return explain.ParseCNF(reader)
}

But having only one type of structure for both packages seems more convenient in my opinion.
Same principle with the problem structure for MaxSAT problem.

If you accept this "improvement", I will propose a PR for it.

Keep me informed @fdelorme

run gophersat

Hi,

In gophersat --verbose file.cnf mode, how can I control output? In fact, I don't want to print solution.

Inconsistent results with/without weight of 0

Let's say we have 4 vars, [1 2 3 4], and their weights are [1 2 0 1] accordingly.

From my instinct, we can use either
solver.GtEq([1, 2, 3, 4], [1, 2, 0, 1], 3)
or
solver.GtEq([1, 2, 4], [1, 2, 1], 3)
to describe the condition of L1 + 2L2 + L4 >= 3

But it turns out the above two representation are not equal.
My failure case is as following, you can also try it in Golang Playground https://play.golang.org/p/ddnTIBx3eZO:

package main

import (
	"fmt"

	"github.com/crillab/gophersat/solver"
)

func main() {
	var lits1, lits2, lits3 []int
	var weights3 []int

	solveAll := func(){
		fmt.Printf("Lits1: %v, Lits2: %v, Lits3: %v, Weights3: %v\n", lits1, lits2, lits3, weights3)
		atMost3 := solver.AtMost(lits1, 3)
		atLeast2 := solver.AtLeast(lits2, 2)
		pbAtLeast3 := solver.GtEq(lits3, weights3, 3)

		pb := solver.ParsePBConstrs(append([]solver.PBConstr{}, atMost3, atLeast2, pbAtLeast3))

		s := solver.New(pb)
		fmt.Println("All solutions", s.CountModels())
	}

	// Case 1
	fmt.Println("Case 1: with weight of 0")
	lits1 = []int{1, 2, 3, 4}
	lits2 = []int{1, 2, 3, 4}
	lits3 = []int{1, 2, 3, 4}

	weights3 = []int{1, 2, 0, 1}
	solveAll()

	// Case 2
	fmt.Println("Case 2: without weight of 0")
	lits1 = []int{1, 2, 3, 4}
	lits2 = []int{1, 2, 3, 4}
	lits3 = []int{1, 2, 4}

	weights3 = []int{1, 2, 1}
	solveAll()
}

Let me know if these two representation are not equal by design or this is a bug.
Thanks!

gometalinter issues

Running gometalinter across the repo brings up a few issues, most of which are cosmetic, but it does flag the following which does look like a real bug:

solver/watcher.go:309:5:warning: ineffective break statement. Did you mean to break out of the outer loop? (SA4011) (megacheck)
solver/watcher.go:317:5:warning: ineffective break statement. Did you mean to break out of the outer loop? (SA4011) (megacheck)

Interpretation of bf.And() and bf.Or()

The tests below fail:

func TestIdentityAnd(t *testing.T) {
	f1 := bf.And(bf.And(bf.Var("x")))
	f2 := bf.And(bf.And(), bf.And(bf.Var("x")))
	m := map[string]bool{"x": true}
	if f1.Eval(m) != f2.Eval(m) {
		t.Fail()
	}
}
func TestIdentityOr(t *testing.T) {
	f1 := bf.Or(bf.Or(bf.Var("x")))
	f2 := bf.Or(bf.Or(), bf.Or(bf.Var("x")))
	m := map[string]bool{"x": false}
	if f1.Eval(m) != f2.Eval(m) {
		t.Fail()
	}
}

Are these expected ?

As
and(and(x), and(y)) == and(and(x, y))
or(or(x), or(y)) == or(or(x, y))
hold,
it may be happy if the following also hold:
and(and(), and(x)) == and(and(x))
or(or(), or(x)) == or(or(x))

For example, emacs lisp returns t for (and), and nil for (or) .

Wrong result (SAT instead of UNSAT)

I run sat solver on the following simple unsatisfiable formula:

p cnf 1 2
1 0
-1 0

And I get the following erroneous response:

c ======================================================================================
c | Number of clauses   :         0                                                    |
c | Number of variables :         1                                                    |
c ======================================================================================
c nb conflicts: 0
c nb restarts: 0
c nb decisions: 0
c nb unit learned: 0
c nb binary learned: 0
c nb learned: 0
c nb clauses deleted: 0
SATISFIABLE
-1 

Panics / infinite loop for certain maxsat instances.

For the following simple example I observed an index out of range error:

func TestSecondBug(t *testing.T) {
	x := maxsat.Var("x")
	//y := maxsat.Var("y")
	hard := maxsat.HardClause(x)
	hard2 := maxsat.HardClause(x)

	problem := maxsat.New(hard, hard2)
	problem.Solve()
}

Also in this case the solver appears to go into an endless loop:

func TestInfiniteLoop(t *testing.T) {
	cs := maxsat.Var("cs")
	p := maxsat.Var("p")
	d1 := maxsat.Var("d1")
	d2 := maxsat.Var("d2")
	t1 := maxsat.Var("t1")
	t2 := maxsat.Var("t2")
	c1 := maxsat.Var("c1")
	c2 := maxsat.Var("c2")

	clauses := []maxsat.Constr {
		maxsat.HardClause(cs),
		maxsat.HardClause(cs.Negation(), p),

		maxsat.SoftClause(p.Negation(), d1, d2),
		maxsat.SoftClause(d1.Negation(), t1),
		maxsat.SoftClause(d1.Negation(), t2),
		maxsat.SoftClause(d2.Negation()),
		maxsat.SoftClause(t1.Negation(), c1),
		maxsat.SoftClause(t2.Negation(), c2),

		maxsat.HardClause(d1.Negation(), d2.Negation()),
		maxsat.HardClause(c1.Negation(), c2.Negation()),
	}


	problem := maxsat.New(clauses...)

	model, cost := problem.Solve()
	fmt.Println(cost)
	fmt.Println(model)

}

Access to maxsat.Problem.solver

I am constructing a MAXSAT problem programmatically (i.e., not through ParseWCNF) and would now like to access the solver in maxsat.Problem. But that component is not exported. Could you export that field or otherwise give access to the solver?

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