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Everything about Difference-map algorithm
The difference-map algorithm is a search algorithm for general constraint satisfaction problems. It is a meta-algorithm in the sense that it is built from more basic algorithms that perform projections onto constraint sets. From a mathematical perspective, the difference-map algorithm is a dynamical system based on a mapping of Euclidean space. Solutions are encoded as fixed points of the mapping. Wikipedia →
🧭 Start here: a 6-step reading path
- Read the overview: Difference-map algorithmA 5-minute orientation
- Skim a recent paper: Direct Phasing of Protein Crystals with Hybrid Difference Map AlgorithmsInfluential work since 2022
- Pick up a classic book: Software EngineeringThe most-reprinted book on the subject
- Read the landmark paper: Quantum Watermark Algorithm Based on Maximum Pixel Difference and Tent MapCited 25 times
- Go sideways: Hybrid input-output algorithmA closely related topic
- Go sideways: Phase retrievalA closely related topic
📖 Definitions
No dictionary entry for this exact phrase.
🗂️ The subject at a glance
🏆 Landmark papers (most cited)
Quantum Watermark Algorithm Based on Maximum Pixel Difference and Tent Map
An image encryption algorithm based on a novel two-dimensional hyperchaotic map and difference algorithm
Timesaving phase retrieval approach based on difference map normalization and fast iterative algorithm
Random phase retrieval approach using Euclidean matrix norm of sum and difference map and fast least-squares algorithm
Rigid registration algorithm based on the minimization of the total variation of the difference map
Quantitative analysis of kernel properties in Kohonen's self-organizing map algorithm: Gaussian and difference of Gaussians neighborhoods
Extension of nonnegative constraints to difference-map algorithm on both real and imaginary parts of real space in phase retrieval
MAP inference algorithms without approximation for collective graphical models on path graphs via discrete difference of convex algorithm
🆕 Influential since 2022
📚 Books worth reading
Software EngineeringRoger S. Pressman, Bruce Maxim · 1982
Clean CodeRobert C. Martin · 2008
Bioinformatics Research and ApplicationsZhipeng Cai · 2013
The Elements of Statistical LearningTrevor Hastie, Robert Tibshirani · 2001
Big data analytics for beginnersFaraz Rabbani · 2014
Parallel Programming in C with MPI and OpenMPMichael J. Quinn · 2003
STL tutorial and reference guideDavid R. Musser, Gillmer J. Derge · 1996
Why programs failAndreas Zeller · 2005
Ranked by number of editions on Open Library, a rough measure of how lasting a book is.
🔗 Related topics
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