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
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 is an intermediate algorithms course note
with an emphasis on teaching techniques for the design and analysis of efficient
algorithms, emphasizing methods of application. Topics include
divide-and-conquer, randomization, dynamic programming, greedy algorithms,
incremental improvement, complexity, and cryptography.
Author(s): Prof. Erik Demaine, Prof. Srinivas
Devadas and Prof. Nancy Lynch
explains core material in data structures and algorithm design, and also helps
students prepare for research in the field of algorithms. Topics covered
includes: Splay Trees, Amortized Time for Splay Trees, Maintaining Disjoint
Sets, Binomial heaps, F-heap, Minimum Spanning Trees, Fredman-Tarjan MST
Algorithm, Light Approximate Shortest Path Trees, Matchings, Hopcroft-Karp
Matching Algorithm, Two Processor Scheduling, Network Flow - Maximum Flow
Problem, The Max Flow Problem and Max-Flow Algorithm.
This note covers the following topics:
Lazy Evaluation and S-Notation, Amortization and Persistence via Lazy
Evaluation, Eliminating Amortization, Lazy Rebuilding, Numerical
Representations, Data-Structural Bootstrapping, Implicit Recursive Slowdown.
This note introduces a number of important algorithm
design techniques as well as basic algorithms that are interesting both from a
theoretical and also practical point of view. Topics covered are: Introduction
to Algorithms, Asymptotic Analysis, Recurrence Equations, Sorting Algorithms,
Search Trees, Randomized Algorithms and Quicksort, Selection Algorithms, Number
Theory and Cryptography Algorithms, Graph algorithms, Greedy Algorithms and
External Memory Algorithms.
Author(s): Department of Computer
Science at Duke University
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.