Are linear algorithms always good for linear problems?
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We exhibit linear problems for which every linear algorithm has infinite error, and show a (mildly) nonlinear algorithm with finite error. The error of this nonlinear algorithm can be arbitrarily small if appropriate information is used. We illustrate these examples by the inversion of a finite Laplace transform, a problem arising in remote sensing.
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- Are linear algorithms always good for linear problems?
Volume 31, Issue 1 , pp 202-212
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- Primary 65R20, 68C05, 68C25
- Secondary 33J35, 45B05, 45L05
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- Author Affiliations
- 1. Department of Computer Science, Columbia University, 10027, New York, NY, USA
- 2. Department of Computer Science, Columbia University, 10027, New York, NY, USA
- 3. Division of Science and Mathematics, Fordham University, College at Lincoln Center, 113 West 60th Street, 10023, New York, NY, USA
- 4. Institute of Informatics, University of Warsaw, PKiN, Warsaw, Poland