more updated content to display on the new site

370/notes/adj-list.md

@@ -1,38 +1,35 @@
-# Adjacency list
+A\* Pathfinding
+===============
 
-Imagine 8 nodes with no connections
+There are 3 main values usedd in reference to A\*:
 
-To store this data in an _adjacency list_ we need __n__ items to store them.
-We'll have 0 __e__dges however so in total our space is (n+e) == (n)
+    f = how promisiing a new location is
+    g = distance from origin
+    h = estimate distance to goal
+    f = g + h
 
-# Adjacency matrix
+For a grid space our `h` is calculated by two straight shots to the goal
+from the current location(ignore barriers). The grid space `g` value is
+basiccally the number of steps we've taken from the origin. We maintain
+a list of potential nodes only, so if one of the seeking nodes gets us
+stuck we can freely remove that, because it succs.
 
-space: O(n^2)
-The convention for notation btw is [x,y] meaning:
-	* _from x to y_
+Time & Space Commplexities
+==========================
 
-# Breadth first search
+Best-First Search
+-----------------
 
-add neighbors of current to queue
-go through current's neighbors and add their neighbors to queue
-add neighbor's neighbors
-	keep going until there are no more neighbors to add
-go through queue and start popping members out of the queue
+Time: O(VlogV + E)
 
-# Depth first search 
+Dijkstra's
+----------
 
-Here we're going deeper into the neighbors
+O(V\^2 + E)
 
-_once we have a starting point_ 
+A\*
+---
 
-_available just means that node has a non-visited neighbor_
-if available go to a neighbor
-if no neighbors available visit
-goto 1
+Worst case is the same as Dijkstra's time
 
-# Kahn Sort
-
-
-# Graph Coloring
-
-When figuring out how many colors we need for the graph, we should note the degree of the graph
+O(V\^2 + E)