Jeremiah Naylor-Trein jtrein

## greedyAlgorithm.js
/**
* Greedy Algorithm (maximum daily profit from stock sale) - "let me see what my options are, first..."
*
* Overview:
* ---------
* By using Greedy Algorithms we can pass over the data once (O(n) time), storing values we find to be optimal, per our criteria, by
* comparing them to current values. We have to go over the entire set to do this, but we only have to do it once - yay!
*
* From Wikipedia: "A greedy algorithm is an algorithm that follows the problem solving heuristic of making the locally optimal
* choice at each stage with the hope of finding a global optimum."

## mergeSortAlgorithm.js
/**
* Merge Sort Algorithm: "divide and conquer", "break you down in order to build you back up"
*   Overview:
*   ---------
*   A recursive sorting algorithm where we 1) divide an unsorted array in half (dismantle big array into tiny sub-arrays)
*   until we reach atomic values (< 2 - it's never 0), then we 2) sort the adjacent sub-arrays (left, right) and
*   3) merge the sorted sub-array values (put things back together) after each iteration
*
*   Time Complexity (worst case):
*   -----
	/**
	* Greedy Algorithm (maximum daily profit from stock sale) - "let me see what my options are, first..."
	*
	* Overview:
	* ---------
	* By using Greedy Algorithms we can pass over the data once (O(n) time), storing values we find to be optimal, per our criteria, by
	* comparing them to current values. We have to go over the entire set to do this, but we only have to do it once - yay!
	*
	* From Wikipedia: "A greedy algorithm is an algorithm that follows the problem solving heuristic of making the locally optimal
	* choice at each stage with the hope of finding a global optimum."
	/**
	* Merge Sort Algorithm: "divide and conquer", "break you down in order to build you back up"
	* Overview:
	* ---------
	* A recursive sorting algorithm where we 1) divide an unsorted array in half (dismantle big array into tiny sub-arrays)
	* until we reach atomic values (< 2 - it's never 0), then we 2) sort the adjacent sub-arrays (left, right) and
	* 3) merge the sorted sub-array values (put things back together) after each iteration
	*
	* Time Complexity (worst case):
	* -----