DSA & Algorithms
38 articles in this topic
Banker's Algorithm for GATE: Safe Sequence Numericals with Full Allocation-Max-Need Matrices
Learn the safety algorithm through a five-process matrix, count every safe sequence, and repair an unsafe state with the smallest possible resource addition.
Page Replacement Algorithms for GATE: FIFO, LRU and Optimal with Belady's Anomaly Explained
One shared reference string traced fault by fault through FIFO, LRU and Optimal page replacement with 3 frames, then re-run under FIFO with 4 frames to demonstrate Belady's anomaly. Includes the counting rules GATE expects, why LRU beating FIFO is a tendency and not a theorem, and the stack-algorithm property that makes LRU and Optimal immune to the anomaly.
Binary Heaps for GATE: Build-Heap, Heapify and Deletion Numericals Solved
Heapify, build-heap and deletion are the three heap procedures GATE CS tests as numericals. The O(n) build-heap bound comes from a height-weighted sum, a seven-element build takes exactly four swaps, and heap questions keep landing in five recognisable shapes.
IBPS SO IT Officer Data Structures and programming: professional knowledge topics
IBPS SO IT Officer Data Structures and programming: arrays, linked lists, stacks, trees, hashing, sorting, complexity and C basics for the Mains PK paper.
DSA interview questions for placements: the patterns freshers must know
Most DSA interview questions are not new. They are variations on a small set of recurring patterns, and once you can recognise the pattern behind a question, the solution follows almost mechanically. Freshers who memorise fifty specific problems stall on the fifty-first. Freshers who learn the seven or eight underlying patterns can attack a problem they have never seen.
Time complexity and asymptotic notation: Big-O, Theta and Omega explained
Time complexity and asymptotic notation explained: Big-O, Theta and Omega, growth-rate ordering, analysing loops and recursion, and the Master Theorem.
Greedy algorithms: strategy, exchange arguments and classic problems
Greedy algorithms explained: greedy-choice property, activity selection, a traced Huffman tree, fractional knapsack, and where greedy fails on 0/1 knapsack.
Hashing and collision resolution: hash functions, chaining and open addressing
Hashing and collision resolution explained: hash functions, load factor, separate chaining, and open addressing with linear, quadratic and double hashing.
Stacks and queues: operations, applications and the exam angle
Stacks and queues explained: LIFO vs FIFO, array and linked implementations, a worked postfix evaluation, and the circular-queue full and empty conditions.
Graph Theory: Euler and Hamiltonian Paths, Coloring and Connectivity
Graph theory for GATE CS: degree and handshaking, Euler vs Hamiltonian paths, planarity and Euler's formula, chromatic number, trees and spanning trees.
Dynamic Programming Explained: optimal substructure and a worked knapsack
Dynamic programming explained: optimal substructure, overlapping subproblems, memoization vs tabulation, and a fully worked 0/1 knapsack DP table example.
Sorting Algorithms Compared: complexity, stability, and the n log n lower bound
Sorting algorithms compared: bubble, insertion, selection, merge, quick and heap sort with best, average and worst time, stability, and the n log n bound.