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Everything about Iterative compression
In computer science, iterative compression is an algorithmic technique for the design of fixed-parameter tractable algorithms, in which one element is added to the problem in each step, and a small solution for the problem prior to the addition is used to help find a small solution to the problem after the step. Wikipedia →
🧭 Start here: a 6-step reading path
- Read the overview: Iterative compressionA 5-minute orientation
- Skim a recent paper: An iterative reference mapping approach for BIM IFCXML classified content compressionInfluential work since 2022
- Pick up a classic book: Krylov methods for compressible flowsThe most-reprinted book on the subject
- Read the landmark paper: Iterative hard thresholding for compressed sensingCited 1,796 times
- Go sideways: Odd cycle transversalA closely related topic
- Go sideways: KernelizationA closely related topic
📖 Definitions
No dictionary entry for this exact phrase.
🗂️ The subject at a glance
🏆 Landmark papers (most cited)
Iterative hard thresholding for compressed sensing
Lossy Compression and Iterative Reconstruction for Encrypted Image
Iterative Dictionary Construction for Compression of Large DNA Data Sets
Compression of Dynamic 3D Geometry Data Using Iterative Closest Point Algorithm
Iterative compression and exact algorithms
Quality controlled ECG data compression based on 2D discrete cosine coefficient filtering and iterative JPEG2000 encoding
Iterative Finite Element Analysis of Concrete-Filled Steel Tube Columns Subjected to Axial Compression
Iterative function system and genetic algorithm-based EEG compression
🆕 Influential since 2022
An iterative reference mapping approach for BIM IFCXML classified content compression
Pixel Optimization Using Iterative Pixel Compression Algorithm for Complementary Metal Oxide Semiconductor Image Sensors
Iterative knowledge distillation and pruning for model compression in unsupervised domain adaptation
Detection of Vertebral Mass and Diagnosis of Spinal Cord Compression in Computed Tomography With Deep Learning Reconstruction: Comparison With Hybrid Iterative Reconstruction
L-Shape-Based Iterative Prediction and Residual Median Edge Detection (LIP-RMED) Algorithm for VVC Intra-Frame Lossless Compression
📚 Books worth reading
- 📕Krylov methods for compressible flowsM. D. Tidriri · 1995
Ranked by number of editions on Open Library, a rough measure of how lasting a book is.
🔗 Related topics
- Odd cycle transversal In graph theory, an odd cycle transversal of an undirected graph is a set of vertices of the graph that has a nonempty intersection with every odd cycle…
- Kernelization In computer science, a kernelization is a technique for designing efficient algorithms that achieve their efficiency by a preprocessing stage in which…
- Bidimensionality Bidimensionality theory characterizes a broad range of graph problems (bidimensional) that admit efficient approximate, fixed-parameter or kernel solutions…
- Biclique-free graph In graph theory, a branch of mathematics, a t-biclique-free graph is a graph that has no Kt,t (complete bipartite graph with 2t vertices) as a subgraph…
- Maximum cut In a graph, a maximum cut is a cut whose size is at least the size of any other cut. That is, it is a partition of the graph's vertices into two complementary…
- Chordal graph In the mathematical area of graph theory, a chordal graph is one in which all cycles of four or more vertices have a chord, which is an edge that is not…
- Parameterized complexity In computer science, parameterized complexity is a branch of computational complexity theory that focuses on classifying computational problems according…
- Bipartite graph In the mathematical field of graph theory, a bipartite graph (or bigraph) is a graph whose vertices can be divided into two disjoint and independent sets…