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A sparse matrix obtained when solving a finite element problem in two dimensions. The non-zero elements are shown in black. In numerical analysis and scientific computing, a sparse matrix or sparse array is a matrix in which most of the elements are zero. [1]
SPARSE contains TALLY, the class of unary languages, since these have at most one string of any one length. Although not all languages in P/poly are sparse, there is a polynomial-time Turing reduction from any language in P/poly to a sparse language. [1] Fortune showed in 1979 that if any sparse language is co-NP-complete, then P = NP. [2]
Sparse is a computer software tool designed to find possible coding faults in the Linux kernel. [2] Unlike other such tools , this static analysis tool was initially designed to only flag constructs that were likely to be of interest to kernel developers, such as the mixing of pointers to user and kernel address spaces .
Sparse dictionary learning (also known as sparse coding or SDL) is a representation learning method which aims to find a sparse representation of the input data in the form of a linear combination of basic elements as well as those basic elements themselves.
Sparse approximation (also known as sparse representation) theory deals with sparse solutions for systems of linear equations. Techniques for finding these solutions and exploiting them in applications have found wide use in image processing , signal processing , machine learning , medical imaging , and more.
Learned sparse retrieval or sparse neural search is an approach to Information Retrieval which uses a sparse vector representation of queries and documents. [1] It borrows techniques both from lexical bag-of-words and vector embedding algorithms, and is claimed to perform better than either alone.
In network science, a sparse network has much fewer links than the possible maximum number of links within that network (the opposite is a dense network). The study of sparse networks is a relatively new area primarily stimulated by the study of real networks, such as social and computer networks.
Disadvantages are that sparse files may become fragmented; file system free space reports may be misleading; filling up file systems containing sparse files can have unexpected effects (such as disk-full or quota-exceeded errors when merely overwriting an existing portion of a file that happened to have been sparse); and copying a sparse file with a program that does not explicitly support ...