期刊
COMPUTATIONAL OPTIMIZATION AND APPLICATIONS
卷 52, 期 1, 页码 293-314出版社
SPRINGER
DOI: 10.1007/s10589-011-9404-4
关键词
Experimental design; Sparsity control; Lanczos bidiagonalization; Super resolution
资金
- National Science Foundation [DMS 0724717, 0724759, 0914987, CCF 0915121]
- NSERC
- IBM OCR
- Direct For Mathematical & Physical Scien
- Division Of Mathematical Sciences [0915121, 0724759] Funding Source: National Science Foundation
- Division Of Mathematical Sciences
- Direct For Mathematical & Physical Scien [0914987] Funding Source: National Science Foundation
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.
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