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.
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 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
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,
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
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.