# Today I Learned

Some of the things I've learned every day since Oct 10, 2016

## 196: Prim’s, Kruskal’s for Maze Generation

Prim’s and Kruskal’s algorithms — or any spanning tree generating algorithm, for that matter — can be used to generate a pseudo-random maze in an $n$-dimensional grid.

For instance, consider the simple example of creating a rectangular maze on a grid like the one below.

Viewing each rectangular ‘cell’ as a graph node and considering all possible walls positioned between 2 cells as edges between those cells’ corresponding nodes, it’s easy to see that a spanning tree in this model produces a pretty decent maze.

Since the tree is spanning across all cells, it’s guaranteed that our maze doesn’t waste any space on the grid. However, the fact that it’s a tree also guarantees that there’s a unique path between any 2 cells, which may or may not be desirable depending on what kind of maze you’re going for. Namely, the number of walls in the maze will be maximal, but we also have that cycles/loops in the maze are impossible.

## 190: A Greedy Scheduling Algorithm

Suppose you have a ‘time’ interval $(a, b)$ with $a, b \in \mathbb{R}$, $a < b$, and that you have a set $E$ of ‘events’ $(s_1, t_1), (s_2, t_2), \dots, (s_n, t_n)$ with $a \leq s_i < t_i \leq b$ for all $i$. And suppose you want to schedule as many events from $E$ into $(a, b)$ as possible. That is, you want to select a subset of non-overlapping members of $E$ of maximum size. The following greedy algorithm does this in an optimal way (optimal solution, not necessarily runtime):

The total runtime is $O(n \log n)$.

## 189: Depth-First, Breadth-First, Uniform Cost, and A* Search Differ Only In Their Fringe

As graph exploration algorithms through search problem state spaces, depth-first, breadth-first, uniform cost, and A* search all have the following general form:

Credit: University of California, Berkeley

They differ only in the type of fringe used. Depth first uses a stack, breadth-first a queue, and both uniform cost and A* use a priority queue. (Uniform cost’s priority is based on the distance from the starting point to a given node, while that of A* is the sum of this distance and the node’s heuristic function.)

## 157: Dijkstra’s and Negative Weights

Dijkstra’s algorithm, commonly used to find the path of minimum weight to any given node from a starting node in a weighted graph, does not work if negative weights are allowed.

Intuitively, the reason is that Dijkstra’s makes the assumption while working through the graph that paths can only become ‘heavier’, and so while visiting a node does not consider neighbors which themselves have already been visited. For instance, consider running the algorithm on the following graph with A as the starting node:

Image: https://stackoverflow.com/q/13159337

The algorithm will look at as a neighbor of A and set its minimum known distance to 2. It will then look at B and set its to 5. It will then move on to visit C without any changes since C has no neighbors. It will then visit B, but will not consider the edge from to C because C has already been visited. So the algorithm will wrongly conclude that the shortest path to C from A is 2, not -5.

One adaptation which does work on certain types of graphs with negative weights is Johnson’s algorithm, which itself uses Dijkstra’s as a subroutine.

## 149: Floyd’s Cycle-Finding Algorithm

Suppose you want to detect a cycle in a linked list. More specifically, you want to find the node $N$ where the cycle begins. One way to do this is Floyd’s cycle-finding algorithm, sometimes referred to as the “Tortoise and the Hare” algorithm.

Informally, the way it works is:

• Initialize two pointers $P, Q$, with $P$ = next(head), $Q$ = next(next(head)).
• While $P \neq Q$, increment $P$ by 1 node and $Q$ by 2. ($P$ is the “slow” pointer and $Q$ is the “fast” one. At each step of this loop the distance between the two will increment by 1.)
• Initialize counter $n = 0$, and reset $P$ to point to the head of the list.
• While $P \neq Q$, increment each of $P, Q$ by 1 node and $n$ by 1.
• Return node $N$ which is $n$ nodes after the head of the list.

The runtime of this algorithm is $O(n + \lambda)$, where $\lambda$ is the period of the cycle being found.

## 124: Skip List Search Algorithm

The following is an informal description of the algorithm for searching for an element in a skip list:

If you look the shape of the nodes that the algorithm visits on a given example, the general case of the search time being $O(\log{} n)$ makes intuitive sense.

## 123: Skip Lists (Intro)

skip list is a data structure similar to a linked list which stores an ordered sequence of elements and is built specifically for fast search within the sequence. The way it’s commonly implemented is as a hierarchy of linked lists, with the first list containing all the elements of the skip list, and each successive linked list containing a sparser subset of the elements in the previous one.

To search for an element within the skip list one then starts with the sparsest linked list, getting as ‘close’ to the desired element as possible, then moves directly down to that same position in the next linked list and continues searching until the item is found (or not).

This animation depicts the insertion of the element 80 into a skip list of positive integers (insertion being equivalent to searching):

Credit: Artyom Kalinin

In the average case, the skip list uses $O(n)$ space and takes just $O(\log{} n)$ time for searching operations.

## 114: Iterative Deepening Search

Iterative deepening depth-first search is a graph traversal algorithm which visits (new) nodes in effectively the same order as breadth-first search, but with lower memory usage and a runtime of $O(b^d)$ (where $b$ is the branching factor of the graph and $d$ is the depth of the goal node.

The algorithm does pretty much what the name describes: it iteratively performs a typical depth-first search up do a set maximum depth, incrementing the depth with each iteration. In this way, nodes are seen for the first time in the order they would be seen by BFS while using lower amounts of memory. In addition, since it runs on DFS the algorithm uses just $O(d)$ space, where $d$ is the depth of the goal.