## Constructing a balanced Binary Search Tree from a sorted List in O(N) time

This post discusses a O(n) algorithm that construct a balanced binary search tree (BST) from a sorted list. For instance, consider that we are given a sorted list: [1,2,3,4,5,6,7]. We have to construct a balanced BST as follows.

        4
|
2        6
|        |
1   3   5     7


To do so, we use the following definition of Tree, described in Scala By Example book.

abstract class IntSet
case object Empty extends IntSet
case class NonEmpty(elem: Int, left: IntSet, right: IntSet) extends IntSet


One straight-forward approach would be to repeatedly perform binary search on the given list to find the median of the list, and then, to construct a balanced BST recursively. Complexity of such approach is O(nlogn), where n is the number of elements in the given list.

A better algorithm constructs balanced BST while iterating the list only once. It begins with the leaf nodes and construct the tree in a bottom-up manner. As such, it avoids repeated binary searches and achieves better runtime complexity (i.e., O(n), where n is the total number of elements in the given list). Following Scala code outlines this algorithm, which effectively converts a list ls to an IntSet, a balanced BST:

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 def toTree(ls: List[Int]): IntSet = { def toTreeAux(ls: List[Int], n: Int): (List[Int], IntSet) = { if (n <= 0) (ls, Empty) else { val (lls, lt) = toTreeAux(ls, n / 2) // construct left sub-tree val x :: xs = lls // extract root node val (xr, rt) = toTreeAux(xs, n - n / 2 - 1) // construct right sub-tree (xr, IntSet(x, lt, rt)) // construct tree } } val (ls_1, tree) = toTreeAux(ls, List.length(ls)) tree }
view raw toTree.scala hosted with ❤ by GitHub

Any comment or query regarding this post is highly appreciated. Thanks.

## “Functional Scala” by Mario Gleichmann

"Functional Scala" is a set of tutorials on Scala programming language by Mario Gleichmann. Although a bit verbose, it introduces the key constructs of Scala, and outlines Scala’s primary features from the perspective of functional programming.

Welcome to the first part of a series of episodes about ‘Functional Scala’. While positioning itself as a so called object-functional language, most of the discussion and articles about Scala centered around its object oriented features so far. If you’re reading this, chances are you want to learn more about the functional side of Scala. Well, you’ve come to the right place.

The idea for the following episodes arose out of some talks i gave about ‘Functional Scala’. I decided to write and talk about it because I wanted to solidify my own knowledge about Functional Programming in general and about Scala in particular … and because I thought I could help some people new to Scala to learn its functional features from my perspective.

Not least, there were some critical discussions in the past whether Scala is rightfully characterized as a functional Language. For this to decide, we firstly have to be clear about the core ideas, you’ll regularly come across within widely accepted functional Languages, like Haskell. We’re going to see if and how they are offered in Scala and try to push them to its limits. So without any further ado, let’s enter the world of Functional Programming (FP) in Scala.

Cheers!

## “The Neophyte’s Guide to Scala” by Daniel Westheide

This Scala tutorial, called "The Neophyte’s Guide to Scala" can be considered as an auxiliary resource of #progfun. It is particularly good at getting started with Scala, and to delve a bit deeper with it.

From November 2012 to April 2013, I created and published a blog series called , targeted at aspiring Scala enthusiasts who have already made their first steps with the language and are looking for more detailed explanations.

Enjoy!

## Comprehensions

Prompted by some recent work I’ve been doing on reasoning about monadic computations, I’ve been looking back at the work from the 1990s by Phil Trinder, Limsoon Wong, Leonidas Fegaras, Torsten Grust, and others, on monad comprehensions as a framework for database queries.

The idea goes back to the adjunction between extension and intension in set theory—you can define a set by its extension, that is by listing its elements:

$latex \displaystyle \{ 1, 9, 25, 49, 81 \} &fg=000000$

or by its intension, that is by characterizing those elements:

$latex \displaystyle \{ n^2 \mid 0 < n < 10 \land n \equiv 1 (\mathop{mod} 2) \} &fg=000000$

Expressions in the latter form are called set comprehensions. They inspired a programming notation in the SETL language from NYU, and have become widely known through list comprehensions in languages like Haskell. The structure needed of sets or…

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