# Programming

## Essence of Programming

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## Minimal models for Propositional Formulas

We look at metaprogramming in Julia. We illustrate parse and eval using the problem of determining a minimal model for a given propositional formula. The computation of minimal models is of interest in answer set programming, as they are used to define stable models 1. The problem of finding a minimal model is NP-complete, whereas the problem of finding a stable model for a given set of propositional statements is $\Sigma_{2}^{P}$ complete.

## Binary Search Trees

Let us implement in julia 0.6.0 a binary search tree to store integers. Each node is either of type Nil or of type bst, as declared below. Type Nil is used a null pointer. We use type union MayBe to hold either a Nil() or a bst. At the start the tree is empty so it is initialized to Nil(). Removing the type declaration from the data element should be sufficient to store datatypes for which a comparator is implemented.

Numbers are all In this post, we examine two examples set out in the classic paper by Turing. The first problem is to report the $n^{th}$ binary digit of 1⁄3. The second problem is to report the $n^{th}$ binary digit of $\sqrt{2}$. The 1936 paper On computable numbers, with an application to the Entscheidungsproble describes two machines to perform the tasks. In this post, we give in Haskell two programs for the same.

In this post, we implement in Haskell, the augmenting path method to find a maximum matching in a bipartite graph. We use function composition and recursion mostly. We obtain a simple implementation of an algorithm to compute maximum matching in a bipartite graph. As a side affect of this exercise, you should know about algorithms to compute terminal objects, and fix points in a category. Only a basic familiarity with Haskell is assumed here.