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.
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.
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
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: Encryption Algorithms, Genetic
Algorithms, Geographic Information Systems Algorithms, Sorting
Algorithms, Search Algorithms, Tree Algorithms, Computational
Geometry Algorithms, Phonetic Algorithms and Project Management