Binary decision diagram software

Sep 3, Download the file for your platform. If you're not sure which to choose, learn more about installing packages. Warning Some features may not work without JavaScript. Please try enabling it if you encounter problems. Search PyPI Search. Latest version Released: Mar 9, Navigation Project description Release history Download files.

Project links Homepage Repository. Maintainers mvcisback. Interface : Description of inputs and outputs of a Multi-valued Decision Diagram. This object is a wrapper around a Binary Decision Diagram object from dd. One hot encoded by default. Can create new variable using same encoding, but different name. All variables must have distinct names.

The encoding slighly differs from the standard reduction [1] from mdds to bdds by assuming the following: If a variable encoding is invalid, then the bdd maps it to 0. Given a good order for performing the Shannon decompositions often results in BDDs that are very small.

Figure 2. The two top levels are annotated with the functions represented. The shaded areas represent isomorphic sub-trees. The two leftmost arrows both point to the same node, hence the c node can be removed. The resulting Reduced Ordered Binary. Decision Diagram is more compact middle. Also real-valued functions can be represented using binary trees, which are called ADDs right. In practice BDDs are not built by starting from a complete tree, which is compressed afterwards, but built bottom-up.

The bottom-up construction uses the standard Boolean connector as operations on BDDs. While negating a BDD is a constant time operation and forming the conjunction or disjunction of two BDDs are polynomial time operations, generating an entire BDD may be exponential in the size of the formula.

In fact, it might grow exponentially, during the bottom-up construction. The size can be reduced by adjusting the order of the variables in the BDD. However, finding the optimal order, which minimizes the size is a co-NP problem [Bryant, ].

Therefore, state-of-the-art BDD implementations use heuristic methods, which often suffice in practice. ADDs are popular as they can compactly represent very large matrices. In an ADD the domain of the terminals isR. When you think of the definition of a BDD, it becomes clear why some orderings are superior to others. What you want in a variable ordering is to decide as much of the function at every decision level as you traverse towards 0 and 1. PyEDA implicitly orders all variables.

It is therefore not possible to create a new BDD by reordering its inputs. You can, however, rename the variables using the compose method to achieve the desired result.

Since BDDs are a memory-constrained data structure, the subject of garbage collection is very important. This is best illustrated with an example. If you look directly into the pyeda. Python EDA latest. Good Variable Ordering.