101 Algorithms Questions You Must Know By Amrinder Arora
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101 Algorithms Questions You Must Know By Amrinder Arora

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59
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3.67 MB
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Digital PDF
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eBook[PDF]
About this ebook
101 Algorithms Questions You Must Know" by Amrinder Arora covers 101 asymptotic complexity questions and answers organized across 11 core algorithmic design techniques and foundational topics. The book is structured to serve as an interview preparation guide and an academic accompaniment for computer science students. [1]
Here is the complete breakdown of the topics and the number of questions dedicated to each section:
Book Sections and Topic Breakdown
  • Warm-up Questions on Math Basics: 9 questions focusing on foundational mathematics and introductory series sums.
  • Asymptotic Analysis and Asymptotic Notation: 19 questions detailing big-O, Omega, and Theta complexities.
  • Data Structures, Sorting & Searching: 3 questions covering fundamental data organization and retrieval methods.
  • Divide and Conquer: 17 questions on breaking problems down into sub-problems (e.g., merge sort or binary search variations).
  • Greedy Algorithms: 8 questions on making locally optimal choices at each stage.
  • Dynamic Programming: 18 questions focusing on optimization problems with overlapping sub-problems and optimal substructure.
  • Graph Traversal and Backtracking (BFS/DFS): 5 questions navigating tree or graph structures.
  • Branch and Bound: 4 questions on state-space search algorithms typically used for optimization problems.
  • NP-Completeness: 9 questions dealing with decision problems, verification, and hard computational limits.
  • Theory of Lower Bounds: 3 questions analyzing the minimum complexity required to solve specific problems.
  • Graph Theory: 6 questions exploring properties and advanced algorithms related to graphs. [1, 2]

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Topics

Asymptotic Analysis and Asymptotic Notation big-O Omega and Theta complexities Branch and Bound: search algorithms typically used for optimization problems Data Structures Sorting & Searching: data organization and retrieval methods Divide and Conquer: breaking problems down into sub-problems Dynamic Programming: problems with overlapping sub-problems and optimal substructure Graph Theory: properties and advanced algorithms related to graphs Graph Traversal and Backtracking (BFS/DFS): tree or graph structures Greedy Algorithms: making locally optimal choices at each stage ISBN-10: 1718157428 ISBN-13: 978-1718157422 Math Basics: foundational mathematics and introductory series sums NP-Completeness: decision problems verification and hard computational limits Theory of Lower Bounds: minimum complexity required to solve specific problems
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