SIAM Journal on Optimization
ACM Transactions on Mathematical Software (TOMS)
Fundamentals of Computerized Tomography: Image Reconstruction from Projections
Fundamentals of Computerized Tomography: Image Reconstruction from Projections
Computational Optimization and Applications
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Finding a feasible point that satisfies a set of constraints is a common task in scientific computing; examples are the linear feasibility problem and the convex feasibility problem. Finitely convergent sequential algorithms can be used for solving such problems; an example of such an algorithm is ART3, which is defined in such a way that its control is cyclic in the sense that during its execution it repeatedly cycles through the given constraints. Previously we found a variant of ART3 whose control is no longer cyclic, but which is still finitely convergent and in practice usually converges faster than ART3. In this article we propose a general methodology for automatic transformation of finitely convergent sequential algorithms in such a way that (1) finite convergence is retained, and (2) the speed of convergence is improved. The first of these properties is proven by mathematical theorems, the second is illustrated by applying the algorithms to a practical problem.