imrajdeepb / algorithms

Implementation of basic data structures & algorithms.

Algorithms

Python implementation of algorithms from CLRS book.

Part 1: Divide and Conquer, Sorting and Searching, and Randomized Algorithms

Primary topics: asymptotic ("Big-oh") notation, sorting and searching, divide and conquer (master method, integer and matrix multiplication, closest pair), and randomized algorithms (QuickSort, contraction algorithm for min cuts).

Part 2: Graph Search, Shortest Paths, and Data Structures

Primary topics: data structures (heaps, balanced search trees, hash tables, bloom filters), graph primitives (applications of breadth-first and depth-first search, connectivity, shortest paths), and their applications (ranging from deduplication to social network analysis).

Part 3: Greedy Algorithms, Minimum Spanning Trees, and Dynamic Programming

Primary topics: greedy algorithms (scheduling, minimum spanning trees, clustering, Huffman codes) and dynamic programming (knapsack, sequence alignment, optimal search trees).

Part 4: Shortest Paths Revisited, NP-Complete Problems and What To Do About Them

Primary topics: shortest paths (Bellman-Ford, Floyd-Warshall, Johnson), NP-completeness and what it means for the algorithm designer, and strategies for coping with computationally intractable problems (analysis of heuristics, local search).