Large sparse matrices---adjacency matrices of web and social graphs, feature matrices of machine-learning pipelines, the relations of graph databases---are routinely compressed to save space. Classical compressors, however, force the matrix to be decompressed before it can be used, so the space savings often yield a computational overhead. A recent line of work studies \emph{computation-friendly} compression: formats that are not only small, but let us carry out matrix--vector products, and in some cases matrix--matrix products, sums and closures, directly on the compressed representation {\em in time proportional to that compressed size}. This survey organizes that literature around the kind of redundancy each format exploits: spatial clustering of the nonzeros ($k^2$-trees and their variants), similarity between rows (Boldi--Vigna-style referencing and set-difference compression), repeated substrings (grammar compression), and dense bipartite substructures (bicliques). For each family we describe the representation, the algorithms, the theoretical guarantees and the empirical performance. Further, we show that the last three families are instances of a single general scheme, which we call a {\em sparse straight-line factorization} of the matrix, whose forward and backward substitutions evaluate right and left products in time proportional to the size of the representation, and that the product of two such factorizations is again one, which gives matrix--matrix products with a compressed output. We also relate the model to linear circuits, give simple recompression steps, and study the size of the smallest factorization: worst-case bounds, separations from each family, and parallelism. We close with a cross-family comparison and a list of open problems.

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