This book
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 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.

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 book provides implementations of common and uncommon
algorithms in pseudocode which is language independent and provides for easy
porting to most imperative programming language. Topics covered includes: Data
Structures, Linked Lists, Binary Search Tree, Heap, Sets, Queues, Algorithms,
Sorting, Sorting.

This
book is designed as a teaching text that covers most standard data structures,
but not all. A few data structures that are not widely adopted are included to illustrate important principles.

This course note
provides an introduction to mathematical modeling of computational problems. It
covers the common algorithms, algorithmic paradigms, and data structures used to
solve these problems.

Author(s): Prof.
Erik Demaine, Prof. Ronald Rivest and Prof. Srinivas Devadas

This
note covers the following topics: Encryption Algorithms, Genetic
Algorithms, Geographic Information Systems Algorithms, Sorting
Algorithms, Search Algorithms, Tree Algorithms, Computational
Geometry Algorithms, Phonetic Algorithms and Project Management
Algorithms.

This book explains the following topics: intrinsic
complexity of computational tasks, Computational Complexity, P, NP,
and NP-Completeness, relations between various computational
phenomena.