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Numerical Methods for A-optimal Designs with a Sparsity Constraint
Computational Optimization and Applications (2012)
  • Zhuojun Magnant, Georgia Southern University

We consider the problem of experimental design for linear ill-posed inverse problems. The minimization of the objective function in the classic A-optimal design is generalized to a Bayes risk minimization with a sparsity constraint. We present efficient algorithms for applications of such designs to large-scale problems. This is done by employing Krylov subspace methods for the solution of a subproblem required to obtain the experiment weights. The performance of the designs and algorithms is illustrated with a one-dimensional magnetotelluric example and an application to two-dimensional super-resolution reconstruction with MRI data.

  • Experimental design,
  • Sparsity control,
  • Lanczos bidiagonalization,
  • Super resolution
Publication Date
Citation Information
Zhuojun Magnant. "Numerical Methods for A-optimal Designs with a Sparsity Constraint" Computational Optimization and Applications Vol. 52 Iss. 1 (2012)
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