Vol. 1, No. 1, 2006

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Problem reduction, renormalization, and memory

Alexandre J. Chorin and Panagiotis Stinis

Vol. 1 (2006), No. 1, 1–27

We present methods for the reduction of the complexity of computational problems, both time-dependent and stationary, together with connections to renormalization, scaling, and irreversible statistical mechanics. Most of the methods have been presented before; what is new here is the common framework which relates the several constructions to each other and to methods of theoretical physics, as well as the analysis of the approximate reductions for time-dependent problems. The key conclusions are: (i) in time dependent problems, it is not in general legitimate to average equations without taking into account memory effects and noise; (ii) mathematical tools developed in physics for carrying out renormalization group transformations yield effective block Monte Carlo methods; (iii) the Mori–Zwanzig formalism, which in principle yields exact reduction methods but is often hard to use, can be tamed by approximation; and (iv) more generally, problem reduction is a search for hidden similarities.

problem reduction, renormalization, irreversible statistical mechanics, memory, Monte Carlo
Mathematical Subject Classification 2000
Primary: 65C20, 65Z05, 82B80, 76F30
Received: 13 April 2005
Accepted: 1 June 2005
Published: 8 May 2007
Alexandre J. Chorin
Department of Mathematics
University of California
Berkeley CA 94720-3840
United States
Panagiotis Stinis
Lawrence Berkeley National Laboratory
1 Cyclotron Road, Mail Stop 50A-1148
Berkeley, CA 94720-1148
United States