Problem 146 (#31)

* WIP solution still failing

* add pseudocode, solution and update README

Author: curtisbarnard

README.md

@@ -73,7 +73,7 @@ As I work through this list I figure I would make a GitHub repo with my solution
 62. Find All Anagrams in a String #438
 63. Minimum Height Trees #310
 64. Task Scheduler #621
-65. **LRU Cache #146**
+65. [LRU Cache #146](https://github.com/curtisbarnard/leetcode-grind75-javascript/blob/main/medium/lru-cache-146.md)
 66. Kth Smallest Element in a BST #230
 
 ### Hard
@@ -134,6 +134,7 @@ In order to practice with similar data structures I'll be placing each problem i
 - [Linked List Cycle #141](https://github.com/curtisbarnard/leetcode-grind75-javascript/blob/main/easy/linked-list-cycle-141.md)
 - [Reverse Linked List #206](https://github.com/curtisbarnard/leetcode-grind75-javascript/blob/main/easy/reverse-linked-list-206.md)
 - [Middle of the Linked List #876](https://github.com/curtisbarnard/leetcode-grind75-javascript/blob/main/easy/middle-linked-list-876.md)
+- [LRU Cache #146](https://github.com/curtisbarnard/leetcode-grind75-javascript/blob/main/medium/lru-cache-146.md)
 
 ### Hash Table
 
@@ -149,6 +150,7 @@ In order to practice with similar data structures I'll be placing each problem i
 - [Longest Substring Without Repeating Characters #3](https://github.com/curtisbarnard/leetcode-grind75-javascript/blob/main/medium/longest-substring-3.md)
 - [3Sum #15](https://github.com/curtisbarnard/leetcode-grind75-javascript/blob/main/medium/3Sum-15.md)
 - [Clone Graph #133](https://github.com/curtisbarnard/leetcode-grind75-javascript/blob/main/medium/clone-graph-133.md)
+- [LRU Cache #146](https://github.com/curtisbarnard/leetcode-grind75-javascript/blob/main/medium/lru-cache-146.md)
 
 ### Binary Tree
 

medium/lru-cache-146.md

@@ -0,0 +1,125 @@
+# LRU Cache
+
+Page on leetcode: https://leetcode.com/problems/lru-cache/
+
+## Problem Statement
+
+Design a data structure that follows the constraints of a Least Recently Used (LRU) cache.
+
+Implement the LRUCache class:
+
+- LRUCache(int capacity) Initialize the LRU cache with positive size capacity.
+- int get(int key) Return the value of the key if the key exists, otherwise return -1.
+- void put(int key, int value) Update the value of the key if the key exists. Otherwise, add the key-value pair to the cache. If the number of keys exceeds the capacity from this operation, evict the least recently used key.
+
+The functions get and put must each run in O(1) average time complexity.
+
+### Constraints
+
+- 1 <= capacity <= 3000
+- 0 <= key <= 104
+- 0 <= value <= 105
+- At most 2 \* 105 calls will be made to get and put.
+
+### Example
+
+```
+Input
+["LRUCache", "put", "put", "get", "put", "get", "put", "get", "get", "get"]
+[[2], [1, 1], [2, 2], [1], [3, 3], [2], [4, 4], [1], [3], [4]]
+Output
+[null, null, null, 1, null, -1, null, -1, 3, 4]
+
+Explanation
+LRUCache lRUCache = new LRUCache(2);
+lRUCache.put(1, 1); // cache is {1=1}
+lRUCache.put(2, 2); // cache is {1=1, 2=2}
+lRUCache.get(1);    // return 1
+lRUCache.put(3, 3); // LRU key was 2, evicts key 2, cache is {1=1, 3=3}
+lRUCache.get(2);    // returns -1 (not found)
+lRUCache.put(4, 4); // LRU key was 1, evicts key 1, cache is {4=4, 3=3}
+lRUCache.get(1);    // return -1 (not found)
+lRUCache.get(3);    // return 3
+lRUCache.get(4);    // return 4
+```
+
+## Solution
+
+### Initial Thoughts
+
+- get and put time complexity of O(1) suggest hashmap and maybe a stack
+- least recently used suggest some sort, based on a counter
+- If I used purely a stack put would be O(1), get would be O(n)
+
+### Optimized Solution
+
+You can see an explanation of the solution here: https://www.youtube.com/watch?v=7ABFKPK2hD4
+
+#### Psuedocode
+
+1. Create a Node class for creating a doubly linked list
+2. Create LRUCache class with two dummy nodes (old and recent), the capacity and a hashmap for the cache
+3. Create get method, removes then inserts node from list and returns if it exist, otherwise returns -1
+4. Create put method, removes node from list if exist, updates cache value, inserts into list
+
+- If cache size is too big, removes oldest node from list and deletes from cache
+
+5. Create remove helper method, takes out a node from list and reconnects adjacent pointers
+6. Create insert helper method, inserts node between "recent" dummy node and next node in list
+
+```javascript
+const Node = function (key, val) {
+  this.key = key;
+  this.val = val;
+  this.prev = null;
+  this.next = null;
+};
+
+const LRUCache = function (capacity) {
+  this.cap = capacity;
+  this.cache = new Map();
+  this.old = new Node('old', 0);
+  this.recent = new Node('recent', 0);
+  this.old.next = this.recent;
+  this.recent.prev = this.old;
+};
+
+LRUCache.prototype.remove = function (node) {
+  prev = node.prev;
+  nxt = node.next;
+  prev.next = nxt;
+  nxt.prev = prev;
+};
+
+LRUCache.prototype.insert = function (node) {
+  prev = this.recent.prev;
+  nxt = this.recent;
+  prev.next = node;
+  nxt.prev = node;
+  node.next = nxt;
+  node.prev = prev;
+};
+
+LRUCache.prototype.get = function (key) {
+  if (this.cache.has(key)) {
+    this.remove(this.cache.get(key));
+    this.insert(this.cache.get(key));
+    return this.cache.get(key).val;
+  }
+  return -1;
+};
+
+LRUCache.prototype.put = function (key, value) {
+  if (this.cache.has(key)) {
+    this.remove(this.cache.get(key));
+  }
+  this.cache.set(key, new Node(key, value));
+  this.insert(this.cache.get(key));
+
+  if (this.cache.size > this.cap) {
+    const lru = this.old.next;
+    this.remove(lru);
+    this.cache.delete(lru.key);
+  }
+};
+```