Sublinear Computation Paradigm Algorithmic Revolution in the Big Data Era /
This open access book gives an overview of cutting-edge work on a new paradigm called the "sublinear computation paradigm," which was proposed in the large multiyear academic research project "Foundations of Innovative Algorithms for Big Data." That project ran from October 2014...
Saved in:
Corporate Author: | |
---|---|
Other Authors: | , , , , , , , |
Format: | Electronic eBook |
Language: | English |
Published: |
Singapore :
Springer Nature Singapore : Imprint: Springer,
2022.
|
Edition: | 1st ed. 2022. |
Subjects: | |
Online Access: | Link to Metadata |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Table of Contents:
- Chapter 1: What is the Sublinear Computation Paradigm?
- Chapter 2: Property Testing on Graphs and Games
- Chapter 3: Constant-Time Algorithms for Continuous Optimization Problems
- Chapter 4: Oracle-based Primal-Dual Algorithms for Packing and Covering Semidefinite Programs
- Chapter 5: Almost Linear Time Algorithms for Some Problems on Dynamic Flow Networks
- Chapter 6: Sublinear Data Structure
- Chapter 7: Compression and Pattern Matching
- Chapter 8: Orthogonal Range Search Data Structures
- Chapter 9: Enhanced RAM Simulation in Succinct Space
- Chapter 10: Review of Sublinear Modeling in Markov Random Fields by Statistical-Mechanical Informatics and Statistical Machine Learning Theory
- Chapter 11: Empirical Bayes Method for Boltzmann Machines
- Chapter 12: Dynamical analysis of quantum annealing
- Chapter 13: Mean-field analysis of Sourlas codes with adiabatic reverse annealing
- Chapter 14: Rigidity theory for protein function analysis and structural accuracy validations
- Chapter 15: Optimization of Evacuating and Walking Home Routes from Osaka City with Big Road Network Data on Nankai Megathrust Earthquake
- Chapter 16: Stream-based Lossless Data Compression.