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For example, given a big data optimization problem formulated as {F}^=underset{x}{ min}left{operatorname{F}(x)=operatorname{f}(x)+operatorname{g}(x);kern0.1em xin {mathrm{mathbb{R}}}^pright} (6).
For example, given a big data optimization problem formulated as {F}^=underset{x}{ min}left{operatorname{F}(x)=operatorname{f}(x)+operatorname{g}(x);kern0.1em xin {mathrm{mathbb{R}}}^pright} (6) where f and g are convex functions.
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To tackle the huge-scale convex and nonconvex big data optimization problems, a novel parallel, hybrid random/deterministic decomposition scheme with the power of dictionary learning was investigated in [114].
Management: This covers potential human and data optimization problems such as personnel and data management issues at participating centres.
In [9], the design is formulated as a sampled-data optimization problem (suppose the primal filters are known) and then solved by standard approximation theory.
The ST2NMR program (Pristovek et al., 2002) casts assignment given a 3D structure and NMR data as an optimization problem, and uses a Monte Carlo approach to find explanations of the data in terms of distances in the structure.
Different from existing uses of MDCs for restoration, the delay in data collection and task balance is considered, and the network connectivity and data acquisition path optimization problem are transformed into an improved multi-travelling salesman problem (iMTSP).
First, a standard NLM is computed based on the initialization data of the optimization problem.
A central database is established to store the input data of the optimization problem.
To identify a tissue specific set of metabolic reactions we applied the algorithm by Shlomi et al. [ 6], which integrates gene expression data with linear optimization problem.
Roughly speaking, Hadamard type well-posedness is based on the continuous dependence of the optimal solution from the data of the considered optimization problem.
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