This note will cover
classic and modern algorithmic ideas that are central to many areas of Computer
Science. The focus is on most powerful paradigms and techniques of how to design
algorithms, and measure their efficiency. The topics will include hashing,
sketching, dimension reduction, linear programming, spectral graph theory,
gradient descent, multiplicative weights, compressed sensing, and others.
explains the following topics: Stable Matchings, Algrithm Design by Induction,
Graphs, Trees or BFS, Connected Comps/Bipartite Graphs, DFS or Topological
Ordering, Interval Scheduling, Interval Partitioning, MST, MST, Union find,
Closest Points, Master Theorem, Integer Multiplication, Median, Vertex Cover or
Set Cover, Network Connectivity, Image Segmentation, Reductions,
NP-Completeness, Linear Programming.
This Lecture Notes is
organized into eleven chapters. Besides the subject matter, each chapter
includes a list of problems and a list of programming projects. Also, each
chapter concludes with a list of references for further reading and exploration
of the subject. Topics covered includes: Lists, Dictionaries, Binary Trees,
Balanced Trees , Priority Queues , Directed Graphs, Undirected Graphs, Sorting
Methods, 0 Introduction to NP-Completeness.
this note is to teach you to program in the C programming language, and to teach
you how to choose, implement, and use data structures and standard programming
techniques. Topics coverd includes: The Zoo and the Zoo Annex, The Linux
programming environment, The C programming language, Data structures and
This note covers the
following topics: Fundamentals of data structure, simple data structures, ideas
for algorithm design, the TABLE Data Type, free storage management, sorting,
storage on external media, variants on the SET Data Type, pseudo-random numbers,
data compression, algorithms on graphs, algorithms on strings and Geometric
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.
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.