Jacob Mattingley

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Branch and bound algorithms are methods for global optimization in nonconvex problems [LW66, Moo91]. They are nonheuristic, in the sense that they maintain a provable upper and lower bound on the (globally) optimal objective value; they terminate with a certificate proving that the suboptimal point found is ǫ-suboptimal. Branch and bound algorithms can be(More)
This chapter concerns the use of convex optimization in real-time embedded systems, in areas such as signal processing, automatic control, real-time estimation , real-time resource allocation and decision making, and fast automated trading. By 'embedded' we mean that the optimization algorithm is part of a larger, fully automated system, that executes(More)
This report presents an algorithm which matches photographs of paintings to a small database. The algorithm uses a modified SIFT approach to match keypoints between paintings. The algorithm is somewhat invariant to size and rotation, highly invariant to perspective change and noise, and can tolerate multiple images in the field of view. Significant(More)
In the clinical laboratory, paper chromatography is still the most useful, simple, inexpensive procedure for initial identification of abnormalities of amino acid excretion. The results of its use for more than 8000 paediatric and adult renal patients is surveyed. Nonspecific generalized aminoaciduria was the most frequent abnormality found, comprising some(More)
1053-5888/10/$26.00©2010IEEE C onvex optimization has been used in signal processing for a long time to choose coefficients for use in fast (linear) algorithms, such as in filter or array design; more recently, it has been used to carry out (nonlinear) processing on the signal itself. Examples of the latter case include total variation denoising, compressed(More)