## Java Developer Roadmap for 2023.

Click on image to see full size. ## Reference Cards (Cheats Sheets) Collection Update.

Reference cards (cheat sheets) collection.

## Pretty fundamental algoritms.

Pretty fundamental:

1. Binary search. Binary search. Binary search. This is an incredibly important concept, and it pops up in everything from convex optimization to databases. If you need to find a particular value in a big set of things, and you can figure out which direction you need to go, use binary search for a MASSIVE speed up.
2. Matrix operations. Now, this one only pops up for me because I’m more of a data scientist, but in general, it’s important to remember that matrix multiplication is faster than you think it is. Brilliant people have come up with incredibly unintuitive algorithms to make matrices multiply in O(n^2.something) instead of O(n^3), like you would expect. In practice, these algorithms aren’t necessarily used, but the BLAS and MPI libraries do provide incredibly fast matrix multiplication all the same. Anytime you can express a bunch of calculations in terms of matrix operations, you win.
3. Linear regression. Simple, beautiful, and useful.
4. Caching. Justuse caching. Does that count as an algorithm? I don’t know, but it is the cause of, and solution to, all of your problems.
5. The idea of a concurrent work queue.
6. Knowing when to throw hashtables at a problem.

## Main algorithms to use.

Basic algorithms :

• Sorting – Merge Sort, Insertion Sort, Quick Sort, Number of inversions
• Matrix Multiplication (just know the algo if not implement it)
• Prime Sieving
• Modular Math including multiplication and division
• Euclidean Algorithm for GCD, Modular Inverse, Fast Exponentiation
• Fibonacci number with matrix multiplication
• Probability distribution and expected value
• Stats – Mean, Median, Variance, Bayes theorem

Linear data structures and algorithms:

• Arrays
• Stack
• Queues

Algorithmic techniques:

• Divide and Conquer – Binary Search, Maximum Subarray
• Greedy Algorithms – Activity Selection, Huffman encoding
• Dynamic Programming – Matrix Chain Multiplication, Knapsack,
• Linear Programming – Variable Maximisation, Linear time sorting
• String Algorithms – Manacher, LCS, Edit Distance

Typical non-linear data structures:

• Trees – Binary Tree, General Tree, Lowest Common Ancestor
• Binary Search Tree – Inorder Traversal, Level order traversal, finding kth largest element, diameter, depth, number of nodes, etc.
• Heaps – Array Implementation, Heapify, Heap Sort
• Union Find
• Hash Table – Linear Probing, Open addressing, Collision avoidance

Graphs:

• Basic Traversal algos – Breadth First Search, Depth First Search, etc
• Shortest Path Finding Algorithm – Dijkstra, Floyd Warshal, Bellman Ford
• Minimum Spanning Tree – Kruskal’s Algo, Prim’s Algo

• Balanced Trees – AVL, Red-Black
• Heavy Light Decomposition, B+ Trees, Quad Tree
• Advance Graph – Min Cut, Max Flow
• Maximum Matching – Hall’s Marriage
• Hamiltonian Cycle
• Edge Graphs / Line Graphs
• Strongly Connected Components
• Dominant Sub-Graph, Vertex Cover, Travelling Salesman – Approx algos

• Knuth Morris Pratt Algorithm
• Rabin Karp Algorithm
• Tries and Compressed Tries
• Prefix Trees, Suffix Trees, Suffix Automation – Ukkonen Algorithm