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Sublinear Computation Paradigm

Overview of attention for book
Sublinear Computation Paradigm
Springer Nature

Table of Contents

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    Book Overview
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    Chapter 1 What Is the Sublinear Computation Paradigm?
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    Chapter 2 Property Testing on Graphs and Games
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    Chapter 3 Constant-Time Algorithms for Continuous Optimization Problems
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    Chapter 4 Oracle-Based Primal-Dual Algorithms for Packing and Covering Semidefinite Programs
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    Chapter 5 Almost Linear Time Algorithms for Some Problems on Dynamic Flow Networks
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    Chapter 6 Information Processing on Compressed Data
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    Chapter 7 Compression and Pattern Matching
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    Chapter 8 Orthogonal Range Search Data Structures
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    Chapter 9 Enhanced RAM Simulation in Succinct Space
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    Chapter 10 Review of Sublinear Modeling in Probabilistic Graphical Models by Statistical Mechanical Informatics and Statistical Machine Learning Theory
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    Chapter 11 Empirical Bayes Method for Boltzmann Machines
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    Chapter 12 Dynamical Analysis of Quantum Annealing
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    Chapter 13 Mean-Field Analysis of Sourlas Codes with Adiabatic Reverse Annealing
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    Chapter 14 Structural and Functional Analysis of Proteins Using Rigidity Theory
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    Chapter 15 Optimization of Evacuation and Walking-Home Routes from Osaka City After a Nankai Megathrust Earthquake Using Road Network Big Data
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    Chapter 16 Stream-Based Lossless Data Compression
Attention for Chapter 3: Constant-Time Algorithms for Continuous Optimization Problems
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Chapter title
Constant-Time Algorithms for Continuous Optimization Problems
Chapter number 3
Book title
Sublinear Computation Paradigm
Published by
Springer, Singapore, October 2021
DOI 10.1007/978-981-16-4095-7_3
Book ISBNs
978-9-81-164094-0, 978-9-81-164095-7
Authors

Yuichi Yoshida, Yoshida, Yuichi

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