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This edition integrates MCQs, quizzes, and practice problems to help learners strengthen both theoretical understanding and practical application. It covers classical and advanced algorithms, asymptotic notations, recursion, graph theory, dynamic programming, NP-completeness, and approximation techniques with real-world examples.
Students will not only learn to design efficient algorithms but also analyze their correctness, performance, and applicability in diverse computing problems.
📂 Chapters & Topics
🔹 Chapter 1: Introduction to Algorithms
Definition and Characteristics
Importance and Applications
Design Goals: Correctness, Efficiency, Simplicity
Pseudocode Conventions
🔹 Chapter 2: Growth of Functions & Asymptotic Notations
Mathematical Preliminaries
Best, Worst & Average Case Analysis
Big-O, Big-Ω, Big-Θ Notations
Growth Rate Comparisons
🔹 Chapter 3: Recursion and Recurrence Relations
Recursion Basics
Recurrence Solving Techniques
Substitution, Iteration, and Master Theorem
🔹 Chapter 4: Divide-and-Conquer Approach
Strategy and Applications
Binary Search, Merge Sort, Quick Sort
Strassen’s Matrix Multiplication
🔹 Chapter 5: Sorting and Searching Algorithms
Basic, Advanced & Linear-Time Sorting
Binary Search and Variations
🔹 Chapter 6: Advanced Data Structures
BST, AVL, Red-Black Trees, B-Trees
Heaps, Priority Queues, and Hashing
🔹 Chapter 7: Greedy Algorithms
Greedy Methodology
MST (Prim’s & Kruskal’s), Huffman Coding
Activity Selection Problem
🔹 Chapter 8: Dynamic Programming
Overlapping Subproblems & Optimal Substructure
Case Studies: Fibonacci, LCS, Knapsack, OBST
🔹 Chapter 9: Graph Algorithms
Representations: Adjacency List/Matrix
BFS, DFS, Topological Sort, SCCs
🔹 Chapter 10: Shortest Path Algorithms
Dijkstra’s Algorithm
Bellman-Ford
Floyd-Warshall & Johnson’s Algorithm
🔹 Chapter 11: Network Flow and Matching
Flow Networks & Ford-Fulkerson
Maximum Bipartite Matching
🔹 Chapter 12: Disjoint Sets and Union-Find
Union by Rank & Path Compression
Applications in Kruskal’s Algorithm
🔹 Chapter 13: Polynomial and Matrix Calculations
Polynomial Multiplication
Fast Fourier Transform (FFT)
Strassen’s Algorithm Revisited
🔹 Chapter 14: String Matching Algorithms
Naïve, Rabin-Karp, KMP, Boyer-Moore
🔹 Chapter 15: NP-Completeness
NP, NP-Hard & NP-Complete Problems
Reductions & Cook’s Theorem
Example Problems (SAT, 3-SAT, Clique, Vertex Cover)
🔹 Chapter 16: Approximation Algorithms
Approximation Ratios
Vertex Cover, TSP, Set Cover
🌟 Why Choose this Book/app?
✅ Covers complete syllabus of Algorithm Design & Analysis
Includes MCQs, quizzes, and practice problems for mastery
✅ Explains recursion, dynamic programming, greedy & graph algorithms in depth
✅ Bridges theory with real-world problem-solving
✅ Perfect for exam preparation, coding interviews, and competitive programming
✍ This app is inspired by authors:
Thomas H. Cormen, Charles Leiserson, Ronald Rivest, Clifford Stein, Jon Kleinberg, Éva Tardos
📥 Download Now!
Master efficiency, complexity, and optimization with Algorithm Design and Analysis (2025–2026 Edition).
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Novinky
- Verze: 1.1
- Aktualizováno:
- 🚀 New Update of Algorithm Design and Analysis
✨ What’s Inside:
✅ Complete syllabus book covering algorithm design, complexity, and analysis
✅ MCQs, quizzes, and practice problems for mastery & exam preparation
🎯 Suitable For:
👩🎓 Students of BSCS, BSIT, Software Engineering & related fields
📘 University & college courses on Algorithms and Data Structures
🏆 Competitive programmers & coding interview preparation
Start mastering algorithms with Algorithm Design and Analysis app! 🚀
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