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Everything about Reduction operator
Computer science concept
In computer science, the reduction operator is a type of operator that is commonly used in parallel programming to reduce the elements of an array into a single result. Reduction operators are associative and often commutative. The reduction of sets of elements is an integral part of programming models such as Map Reduce, where a reduction operator is applied (mapped) to all elements before they are reduced. Other parallel algorithms use reduction operators as primary operations to solve more complex problems. Many reduction operators can be used for broadcasting to distribute data to all processors. Wikipedia →
🧭 Start here: a 6-step reading path
- Read the overview: Reduction operatorA 5-minute orientation
- Skim a recent paper: Memory, evolutionary operator, and local search based improved Grey Wolf Optimizer with linear population size reduction techniqueInfluential work since 2022
- Pick up a classic book: Circular Economy for the Management of OperationsThe most-reprinted book on the subject
- Read the landmark paper: Data-driven operator inference for nonintrusive projection-based model reductionCited 457 times
- Go sideways: Multidimensional scalingA closely related topic
- Go sideways: Householder transformationA closely related topic
📖 Definitions
No dictionary entry for this exact phrase.
🗂️ The subject at a glance
🏆 Landmark papers (most cited)
Data-driven operator inference for nonintrusive projection-based model reduction
Data-Driven Model Reduction and Transfer Operator Approximation
Koopman operator-based model reduction for switched-system control of PDEs
Operator inference for non-intrusive model reduction with quadratic manifolds
An algorithm for reduction of operator strength
Cohomology of operator algebras. III : reduction to normal cohomology
Operator monotones, the reduction criterion and the relative entropy
Model Reduction of Kinetic Equations by Operator Projection
🆕 Influential since 2022
Memory, evolutionary operator, and local search based improved Grey Wolf Optimizer with linear population size reduction technique
Deep-HyROMnet: A Deep Learning-Based Operator Approximation for Hyper-Reduction of Nonlinear Parametrized PDEs
Higher-order-operator corrections to phase-transition parameters in dimensional reduction
Lagrangian operator inference enhanced with structure-preserving machine learning for nonintrusive model reduction of mechanical systems
Generalization error guaranteed auto-encoder-based nonlinear model reduction for operator learning
📚 Books worth reading
Circular Economy for the Management of OperationsAnil Kumar, Jose Arturo Garza-Reyes · 2020- 📕Sparse ModelingIrina Rish, Genady Grabarnik · 2014
Strategic planning models for reverse and closed-loop supply chainsKishore K. Pochampally, Surendra M. Gupta · 2008
The Kaizen event plannerKaren Martin, Mike Osterling · 2007- 📕Hearing on National Defense Authorization Act for Fiscal Year 2015 and oversight of previously authorized programs before the Committee on Armed Services, House of Representatives, One Hundred Thirteenth Congress, second sessionEmerging Threats and Capabilities United States. Congress. House. Committee on Armed Services. Subcommittee on Intelligence · 2014
Lean in AgricultureVibeke Fladkjaer Nielsen, Susanne Pejstrup · 2018- 📕Feasibility of NASA TT&C via commercial satellite servicesW. Carl Mitchell · 1997
- 📕Artillería española en IndiasAngel Corrales Elhordoy · 1989
Ranked by number of editions on Open Library, a rough measure of how lasting a book is.
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