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A data compression method applicable to first‐order convergent iterative procedures
Author(s) -
Shepard Ron
Publication year - 1990
Publication title -
journal of computational chemistry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.907
H-Index - 188
eISSN - 1096-987X
pISSN - 0192-8651
DOI - 10.1002/jcc.540110105
Subject(s) - algorithm , computer science , convergence (economics) , subspace topology , iterative method , representation (politics) , truncation (statistics) , truncation error , compression (physics) , data compression , rate of convergence , mathematics , mathematical optimization , artificial intelligence , computer network , channel (broadcasting) , materials science , machine learning , politics , political science , law , economics , composite material , economic growth
Abstract A data compression method is presented that is generally applicable to first‐order convergent iterative procedures that employ subspace expansions or extrapolations based on successive correction vectors. This method is based on the truncation of insignificant information in successive correction vectors. Although the correction vectors themselves may be severely truncated with the proposed approach, the final solution vector may be represented to arbitrary accuracy. A feature of the proposed method is that more slowly convergent iterative procedures allow the correction vectors to be more severely truncated without affecting the overall convergence rate. The method is implemented and applied to the iterative Davidson diagonalization method. If the compressed representation of the expansion vectors can be held in main computer memory, then a significant reduction in the I/O requirements is achieved.