This note covers the following topics: Computational Models,
Complexity measures, Power increasing resourses, Basic relatton
amomg the models and measures, Reducibility, completeness and
closure under reductions, Deterministics and nondeterministics
logarithmic space, Deterministics polynomial time, Polynomial
Hierarchy and Polynomial space.
note will examine various data structures for storing and accessing information
together with relationships between the items being stored, and algorithms for efficiently
finding solutions to various problems, both relative to the data structures and
queries and operations based on the relationships between the items stored.
Topics covered includes: Algorithm analysis, List, stacks and queues, Trees and
hierarchical orders, Ordered trees, Search trees, Priority queues, Sorting
algorithms, Hash functions and hash tables, Equivalence relations and disjoint
sets, Graph algorithms, Algorithm design and Theory of computation.
In computer science, an
algorithm is a self-contained step-by-step set of operations to be performed.
Topics covered includes: Algorithmic Primitives for Graphs, Greedy Algorithms,
Divide and Conquer, Dynamic Programming, Network Flow, NP and Computational
Intractability, PSPACE, Approximation Algorithms, Local Search, Randomized
This note covers the following
topics: Introduction to Algorithms, Asymptotic Notation, Modeling or Logarithms,
Elementary Data Structures, Dictionary data structures, Sorting, Heapsort or
Priority Queues, Recurrence Relations, Introduction to NP-completeness,
Reductions, Cook's Theorem or Harder Reduction, NP-completeness challenge,
Approximation Algorithms and Heuristic Methods.
lecture note discusses the approaches to designing optimization algorithms,
including dynamic programming and greedy algorithms, graph algorithms, minimum
spanning trees, shortest paths, and network flows. Also it briefly discusses
algorithmic problems arising from geometric settings, that is, computational
note covers the following topics related to Algorithm Analysis and Design: Model
and Analysis, Warm up problems, Brute force and Greedy strategy, Dynamic
Programming, Searching, Multidimensional Searching and Geometric algorithms,
Fast Fourier Transform and Applictions, String matching and finger printing,
Graph Algorithms, NP Completeness and Approximation Algorithms.
The material of this book is aimed at advanced
undergraduate information (or computer) science students,
postgraduate library science students, and research workers in the
field of IR. Some of the chapters, particular chapter 6, make simple
use of a little advanced mathematics.