BDD
Introduction
A Binary Decision Diagram (BDD) is a directed acyclic graph of a given formula. It has a single root; Each inner node is labeled with a propositional variable and has one outgoing edge for a positive assignment, and one edge for a negative assignment of the respective variable. The leaves are labeled with 1 and 0 representingtrue and false. An assignment is represented by a path from the root node to a leaf and its evaluation is the respective value of the leaf. Therefore, all paths to a 1-leaf are valid (possibly partial) models for the formula.
LogicNG's BDD implementation is a Java implementation of the BDD package BuDDy. All classes in the package jbuddy are part of the implementation. The data structures in this package are very low-level and only used for working with BDDs internally. If you want a "nicer" data structure for working with BDDs you can use the LogicNG BDD data structures in the package datastructures which lean more on a graphical representation of BDDs. One can create the LogicNG internal data structure of a BDD using the LNGBDDFunction, see below.
Some content in this chapter, such as the description of BDDs above and the example below are taken from chapter 2.3.1 in New Formal Methods for Automotive Configuration.
Example
As an example for a BDD, consider the formula
The BDD of f1 is:
graph TD
id0["false"]
style id0 stroke:#ea2027,color:#ffffff,fill:#ea2027
id1["true"]
style id1 stroke:#009432,color:#ffffff,fill:#009432
id6(["x3"])
id11(["x2"])
id12(["x2"])
id13(["x1"])
id6 --> id0
linkStyle 0 stroke:#ea2027,stroke-width:2px,stroke-dasharray:3
id6 --> id1
linkStyle 1 stroke:#009432,stroke-width:2px
id11 --> id1
linkStyle 2 stroke:#ea2027,stroke-width:2px,stroke-dasharray:3
id11 --> id6
linkStyle 3 stroke:#009432,stroke-width:2px
id12 --> id6
linkStyle 4 stroke:#ea2027,stroke-width:2px,stroke-dasharray:3
id12 --> id1
linkStyle 5 stroke:#009432,stroke-width:2px
id13 --> id11
linkStyle 6 stroke:#ea2027,stroke-width:2px,stroke-dasharray:3
id13 --> id12
linkStyle 7 stroke:#009432,stroke-width:2pxThe green lines represent positive assignments of the respective variable, the red dotted lines represent negative assignments of the respective variable. That is, for some given assignment:
If, in this assignment, x1 = 1 (meaning x1 is assigned to true), then we follow the green path, else we follow the red path. Then, at x2, check if x2 = 1 in the given assignment. If it is, follow the green path, else follow the red path, etc. The applied variable ordering is x1 > x2 > x3, meaning that on any path the variables appear in this order. As we will find out below, the variable ordering plays a key role for the size of the BDD. In order to see that the BDD graph indeed represents f1, one can try out some data points. For example,
f1(1,0,1) = 1f1(1,0,0) = 0
Creating a BDD graph
You can create your own BDD graph in the dot format using the BDDDotFileWriter. The syntax is, for a given formula f1:
Variable Ordering
The structure and the size (number of nodes) of a BDD strongly depends on the specified variable ordering. There are examples where a bad variable ordering produces an exponential size BDD and a good variable ordering can lead to a linear size BDD.
Consider the formula
-
Given the variable ordering
the resulting BDD has more than2^nnodes. -
Given the variable ordering
the BDD consists of2n + 2nodes.
For more information see wikipedia.
However, finding an optimal variable ordering is NP-complete.
In LogicNG, some common heuristics for BDD variable orderings are implemented:
Sorting Based on Variables' Occurrences
The variable orderings MinToMaxOrdering and MaxToMinOrdering sort the variables from minimal to maximal (respectively, maximal to minimal) occurrence in the input formula. If two variables have the same number of occurrences, their ordering according to their DFS ordering (see below) will be considered.
Sorting Based on Breath-First-Search and Depth-First-Search
The DFSOrdering traverses the formula in a DFS manner and gathers all variables in the occurrence. Analogously, the BFSOrdering traverses the formula in a BFS manner and gathers all variables in the occurrence. Check out how to traverse a formula in DFS manner (and, respectively, BFS manner) here.
Force Ordering
The ForceOrdering is a simple implementation of the "FORCE" variable ordering heuristic based on hyper-graphs presented by Aloul, Markov, and Sakallah. This ordering only works for CNF formulas, thus the given formula has to be converted to CNF before this ordering is called. One can transform a formula to CNF using one of the CNF transformations.
The code snippet to create a variable ordering (here with the example of a DFSOrdering) for a formula f1 is:
A practical example for the significance of the right variable ordering is the following:
Consider the following formula f1 which we want to transform into a BDD and compute the number of clauses of the CNF representation of the BDD.
final Formula f1 = p.parse("(~(v372 | v2095 | v2096 | v683 | v1629 | v1655 | v1487 | v141 | v1509 | v743 | v2137 | v1622 | v2276 | v811 | v2277 | v782 | v39 | " +
"v1900 | v1375 | v1376 | v2113 | v2114 | v17 | v18) & v1604 | (v17 | v18) & ~(v372 | v2095 | v2096 | v683 | v1455 | v1655 | v1487 | " +
"v1509 | v743 | v2137 | v1356 | v1622 | v2276 | v811 | v2277 | v782 | v39 | v1900 | v1375 | v1376 | v2113 | v2114) & v1604) & (v1421 " +
"& v1455 & v1457 & v675 & v676 & v690 & v504 & v708 & v405 & v1669 & v1467 & v1466 & v1570 & v1472 & v1493 & v1454 & v507 & v695 & " +
"v1469 & v1481 & ~(v17 | v18) & v1604 | (v17 | v18) & v1457 & v675 & v676 & v690 & (v707 | v708) & v2254 & v405 & v1669 & v1361 & " +
"v1563 & v1467 & v1466 & v1570 & v1493 & v695 & v508 & v1469 & v1474 & v1483 & v1604) & (v1539 & v356 & v772 & v769 & v987 & v1607 & " +
"(v1645 | v506) & v790 & v974 & v1486 & v512 & (v865 | v866 | v876 | v886 | v891) & v854 & v696 & (v801 | v836 | v832) & (v316 | " +
"v344) & v1604 | v1539 & v357 & v682 & v1350 & v684 & v511 & v1449 & (v886 | v891 | v864 | v865 | v866) & v1354 & v1607 & v2172 & " +
"v2117 & v862 & (v836 | v827 | v832) & v1604 | v1539 & v358 & v1640 & v769 & v1607 & v682 & v790 & v684 & v1486 & v511 & v1449 & " +
"v1354 & v1356 & (v865 | v866 | v876 | v886 | v891) & v854 & v506 & v862 & (v836 | v827 | v832) & (v316 | v344) & v1604 | v17 & v356 " +
"& v770 & v769 & (v500 | v503) & v512 & v1486 & (v865 | v886 | v891 | v898) & v854 & v1424 & (v836 | v1209 | v837 | v828) & (v316 | " +
"v344) & v1604 | v17 & v771 & v770 & v769 & v1374 & v1604 | v17 & v505 & v769 & v682 & v684 & v1486 & v511 & v1449 & v1354 & (v865 | " +
"v886 | v891 | v898) & v854 & v503 & v1424 & v862 & v1494 & v361 & (v836 | v837 | v827 | v828) & (v316 | v344) & v1604 | v17 & v359 &" +
" v682 & v1625 & v1350 & v684 & v511 & v1449 & (v865 | v886 | v891 | v898) & v1354 & v1607 & v2172 & v2117 & v862 & v1494 & v361 & " +
"(v836 | v837 | v827 | v828) & v1604) & (v1539 & v357 & v1604 | ~(v1401 | v781 | v684 | v1449 | v1354 | v2117 | v1494) & v1604 | v17 " +
"& v505) & v391 & v1604");
final BDD bdd = formula.bdd(VariableOrdering.BFS); // (1)!
final BigInteger numberOfClauses = bdd.numberOfClausesCNF();
- replace respectively by the other orderings
Depending on the variable ordering, we find that the resulting BDD has a very different number of CNF clauses:
| Variable Ordering | Number of Clauses |
|---|---|
MAX2MIN |
5.646.676.904 |
FORCE |
22.429.167 |
DFS |
8.963.730 |
MIN2MAX |
3.685.413 |
BFS |
1.182.347 |
So the number of clauses in the CNF varies from 1.182 million to 5.646 billion, depending on the variable ordering. This is a factor of ~5.000!
Warning
This example does not mean, that in general the BFS heuristic performs better than any other heuristic. One could easily find another example formula where the numbers are exactly opposite.
BDD Reorderings
When you created a BDD with a given ordering and want to improve the ordering, you can reorder the given BDD. Reordering a BDD is usually more efficient than creating a new one with a different ordering. For example, consider the following formula and its respective BDD:
We can perform a random reordering with a BDDReordering in the following way:
BDDReordering bddReordering = new BDDReordering(bdd.underlyingKernel()); // (1)!
bddReordering.addVariableBlockAll(); // (2)!
bddReordering.reorder(BDDReorderingMethod.BDD_REORDER_RANDOM); // (3)!
- create a BDDReordering
- adds a single variable block for all variables known by the kernel
- reorders randomly
The result is:
| Original BDD | BDD after Reordering |
|---|---|
|
|
Note that the command bddReordering.addVariableBlockAll() is necessary: It means that the reordering should happen between all variables. Alternatively, you can define blocks of variables in which you want to reorder using the method addVariableBlock().
Creating BDDs
There are essentially two ways to create a BDD: The first one uses the BDDFactory directly, the second is a "shortcut" and uses the bdd()-function on the respective formula.
In this chapter, consider the formula f1 and the variable ordering varOrder.
Creating BDDs Using a BDD Factory
Firstly, we consider the option of using a BDDFactory. BDDs are created with a kernel and, optionally, with a handler. The kernel holds all internal data structures and the internal state for the BDD creation, the handler can control the creation process.
BDD Kernel
Let n be the number of variables of f1. The constructor parameters of the BDDKernel are the following. One of numVars and ordering must be present.
| Parameter | Forced | Default Value | Description |
|---|---|---|---|
f |
yes | Formula factory of f1 |
The formula factory to be used for the creation |
numVars |
no | n |
The number of variables |
ordering |
no | (none) | The variable ordering |
nodeSize |
yes | 30 * n |
The initial number of nodes in the node table |
cacheSize |
yes | 20 * n |
The fixed size of the internal caches. |
Initial nodeSize
The BDD kernel internally holds a table with all nodes in the BDD. This table can be extended dynamically, but this is an expensive operation. On the other hand, one wants to avoid reserving too much space for nodes, since this costs unnecessary memory. 30 * x proved to be efficient in practice for medium sized formulas.
A kernel can be constructed without a variable order, or with an ordering:
Without a variable ordering
For example, with numVars, nodeSize and cacheSize all being 10, then the constructor is:
The variable ordering is then the ordering in which the variables occur in the formula. This is obviously not a very sensible ordering, therefore it is recommended to create a kernel with a variable ordering.
With a variable ordering
For example, with the variable ordering MaxToMinOrdering and nodeSize and cacheSize all being 10, the constructor is:
List<Variable> ordering = new MaxToMinOrdering().getOrder(f1);
BDDKernel kernel2 = new BDDKernel(f1.factory(), ordering, 10, 10);
When you don't define the BDD kernel for your applications, the standard kernel configuration with the default values from the table above will be used.
BDD Handler
Using a BDDHandler, one can control the BDD compilation. There are two BDD handler implemented in LogicNG:
- The TimeoutBDDHandler cancels the compilation of the BDD after the given timeout. There are different timeout types which are described in the class itself.
- The NumberOfNodesBDDHandler cancels the compilation of the BDD after a given number of added nodes.
Of course you can always implement your own handler for specific purposes. To initialize the BDD factory with a handler, see the following code snippet:
BDDKernel myKernel = new BDDKernel(f1.factory(), f1.variables().size(), 10, 15);
BDDHandler myHandler = new TimeoutBDDHandler(100);
BDD bdd = BDDFactory.build(f1, myKernel, myHandler);
Create a BDD without a BDD Factory
The class Formula has a method to create BDDs. With this method one cannot set a handler or settings via a kernel. Since without a BDD handler it is impossible to control the building process, these methods should only be called for small formulas. The kernel is the standard kernel (see above).
- Without a variable ordering:
f1.bdd()returns the BDD off1with the variable ordering like the variables occurr in the formula. - With a variable ordering:
f1.bdd(varOrder)returns the BDD off1with the variable orderingvarOrder.
Creating BDDs by Executing Boolean Operations
If you already have a BDD, you can directly execute Boolean operations on it and e.g. negate it, or conjoin or disjoin it with another BDD. The available operations on the BDD class are:
negate()build a negation of the current BDDimplies(BDD other)build an implication from the current to theotherBDDisImpliedBy(BDD other)build an implication from theotherBDD to the current oneequivalence(BDD other)build an equivalence between the current and theotherBDDand(BDD other)build the conjunction of this and theotherBDDor(BDD other)build the disjunction of this and theotherBDD
Functions on BDDs
When given a formula in the BDD format, one can execute different functions on them. For this section, consider the BDD created above:
that is:graph TD
id0["false"]
style id0 stroke:#ea2027,color:#ffffff,fill:#ea2027
id1["true"]
style id1 stroke:#009432,color:#ffffff,fill:#009432
id6(["x3"])
id11(["x2"])
id12(["x2"])
id13(["x1"])
id6 --> id0
linkStyle 0 stroke:#ea2027,stroke-width:2px,stroke-dasharray:3
id6 --> id1
linkStyle 1 stroke:#009432,stroke-width:2px
id11 --> id1
linkStyle 2 stroke:#ea2027,stroke-width:2px,stroke-dasharray:3
id11 --> id6
linkStyle 3 stroke:#009432,stroke-width:2px
id12 --> id6
linkStyle 4 stroke:#ea2027,stroke-width:2px,stroke-dasharray:3
id12 --> id1
linkStyle 5 stroke:#009432,stroke-width:2px
id13 --> id11
linkStyle 6 stroke:#ea2027,stroke-width:2px,stroke-dasharray:3
id13 --> id12
linkStyle 7 stroke:#009432,stroke-width:2pxThere are a couple of functions to query the internal properties of the BDD:
underlyingKernel()returns the kernel of the BDDbddNodeCount()counts the number of distinct nodes for the BDD. Forbdd, it is4getVariableOrder()returns the variable order of the BDD. Forbdd, it is[x1, x2, x3]pathCountZero()andpathCountOne()return the number of paths leading to the terminal0(false), respectively,1(true) node
Tautology and Contradiction Check
To check whether a BDD is a tautology or a contradiction, you can call the two methods isTautology() and isContradiction(). This is one of the questions which can be answered very easily and in fact in constant time on a BDD: a tautology BDD has only one node 1 and a contradiction has only one node 0.
Variable Profile
The method variableProfile() computes the how often each variable occurs in the BDD, i.e. how many nodes are there for each variable. The syntax is:
The result is {x1=1, x2=2, x3=1}, meaning that x1 occurs once in the BDD, x2 occurs twice in the BDD and x3 occurs also just once in the BDD.
Different Variable Profiles
Note that the result of the variableProfile of the BDD is usually different from the variable profile on the original formula:
Formula f1 = p.parse("(x1 <=> x2) | x3"); // the initial formula of bdd
Map<Variable, Integer> variableProfile = f1.apply(new VariableProfileFunction());
The result is {x1=1, x3=1, x2=1}, as x2 occurs in the formula only once.
Variable Support
The support of a BDD is the set of variables which the BDD depends on, meaning the opposite of don't-care variables. The support is not necessarily equivalent to the set of variables of the formula with which the BDD was created. In order to see this, consider the example of a tautology:
Then the support of the formula can be computed via
Thus, the support is empty, but the variables off2 are A, B. If we added a variable which is relevant to the BDD to the formula, say
then the support is C, as it is the only variable which the BDD depends on.
Swapping Variables in the Orderung
You can change the initial variable ordering manually (in contrast to automatic variable reordering) using swapVariables(). For example, swapping the variables x1 and x2 can be achieved with:
The result is:
| Original BDD | BDD after Swapping x1 and x2 |
|---|---|
|
|
CNF and DNF generation
CNF and DNF creation on a BDD is very easy: For a DNF just enumerate all paths to the 1-node, for a CNF enumerate all paths to the 0-node and negate them. The function numberOfClausesCNF() counts the number of clauses of the CNF without building the CNF.
This is very helpful if you want to know if the resulting CNF is feasible to work with or not. A seen in the examples above: harmless looking formulas can generate CNFs with more than 5.000 clauses. But sometimes the CNF generated with the BDD can be a viable alternative for a factorization-based CNF without auxilliary variables. Therefore there is a FormulaTransformation you can use directly on formulas to generate a BDD-based CNF: BDDCNFTransformation.
An example for these functions is:
The number of clauses is 2 and the result is
(x1 | ~x2 | x3) & (~x1 | x2 | x3).
2.3.0 The method dnf() on a BDD returns the DNF of the BDD in the same way as the above desribed CNF. There is also a formula transformation BDDDNFTransformation to use a BDD-based DNF generation directly on a formula.
Model Counting and Enumeration
Counting and listing all possible models of a BDD is simple and fast since this simply requires following all paths which lead to "true". This is another algorithmic approach for model counting and enumeration. For the example of f1, the syntax is
The model count is 6 and the resulting models are:
enumerateAllModels() has an optional parameter if you want to specify the set of variables over which you enumerate (just like the enumerateAllModels() method on the solver) for projected model enumeration. For more information, see the chapter on model enumeration.
Finding One Model
If you are only interested in one model, there are three options to find one:
Firstly, you can use model(), which returns the variables of one path which leads to "true". This assignment is not complete; it contains only those variables which were on the path to true, which can be a subset of all variables occurring in the BDD. All variables not occuring are don't-care variables.
For example:
returnsAssignment{pos=[], neg=[~x2, ~x1]}.
This is not a complete model, since x3 is not assigned it is a don't-care varialbe, meaning that adding either ~x3 or x3 to the partial assignment results in a valid model.
Secondly, if you want to specify which value the don't-care variables in the resulting model should have, you can use the method model() with extra parameters. The first parameter specifies to which value the don't-care variables should be set in the model (true/false), and the second parameter specifies which variables should be contained in the model.
For example,
Assignment model1 = bdd.model(true, f1.variables()); // (1)!
Assignment model2 = bdd.model(false, f1.variables()); // (2)!
- don't-care variables are set to
true - don't-care variables are set to
false
returns the following results:
model1:Assignment{pos=[x3], neg=[~x2, ~x1]}model2:Assignment{pos=[], neg=[~x3, ~x2, ~x1]}
Thirdly, if you don't-care what value the don't-care variables have, you can use the method fullModel(). This method returns a complete model with an arbitrary assignment of the don't care variables.
The syntax is:
and the result is Assignment{pos=[], neg=[~x3, ~x2, ~x1]}.
Restriction
Using the function restrict() one can restrict the BDD with literals, i.e. setting the variables to a fixed value and simplifying the BDD - like restriction on formulas. As an example, we restrict the given BDD by x2.
The result is:
| Original BDD | BDD after Restricting x2 |
|---|---|
|
|
Quantifier Elimination
You can perform existential and universal quantifier elimination for a given set of variables using the exists() and forall() methods. An introduction to Boolean quantifier elimination can be found e.g. in Parametric Quantified SAT Solving by Sturm and Zengler. For example, if we want to eliminate the universally quantified variable x3 from the BDD, we could perform the following code:
The result is
| Original BDD | BDD after Universally Eliminating x3 |
|---|---|
|
|
Converting to a Formula
Additionally to a CNF and a DNF, also the Shannon expansion can be generated from a given BDD with the method toFormula()
yielding the formula representation ~x1 & (~x2 | x2 & x3) | x1 & (~x2 & x3 | x2).
Creating the LogicNG-internal BDD Data Structure
Sometimes it can be useful to create a data structure which leans on the graphical representation of BDDs. Consider the representation of f1 from above:
graph TD
id0["false"]
style id0 stroke:#ea2027,color:#ffffff,fill:#ea2027
id1["true"]
style id1 stroke:#009432,color:#ffffff,fill:#009432
id6(["x3"])
id11(["x2"])
id12(["x2"])
id13(["x1"])
id6 --> id0
linkStyle 0 stroke:#ea2027,stroke-width:2px,stroke-dasharray:3
id6 --> id1
linkStyle 1 stroke:#009432,stroke-width:2px
id11 --> id1
linkStyle 2 stroke:#ea2027,stroke-width:2px,stroke-dasharray:3
id11 --> id6
linkStyle 3 stroke:#009432,stroke-width:2px
id12 --> id6
linkStyle 4 stroke:#ea2027,stroke-width:2px,stroke-dasharray:3
id12 --> id1
linkStyle 5 stroke:#009432,stroke-width:2px
id13 --> id11
linkStyle 6 stroke:#ea2027,stroke-width:2px,stroke-dasharray:3
id13 --> id12
linkStyle 7 stroke:#009432,stroke-width:2pxThe LogicNG internal BDD data structure has a super class for all nodes: BDDNode, a class for inner nodes representing variables BDDInnerNode, and a class for terminals of the BDD BDDConstant. Any inner node holds the variable it represents, it's high-edge, meaning the variable is assigned to true (green line in graph), and it's low-edge (red dotted line in graph), meaning the variable is assigned to false.
This LogicNG internal data structure of a given BDD can be created using the LNGBDDFunction or with the auxiliary method toLngBdd().
For example, given
then the result of the function is the representation of f1 with the LogicNG internal representation: