Summing and nuclear norms in Banach space theory

By G. Jameson. 1987

How researchers see this paper

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Sequential Kernel Herding: Frank-Wolfe Optimization for Particle Filtering (2015, positive): “We consider the nuclear norm (Jameson, 1987): ‖ft‖Ft⊗Ht = inf{αi,βi}∞i=1 ∑ i ‖αi‖Ft‖βi‖Ht over all possible decompositions {αi, βi}∞i=1 of ft such that, for all xt, xt+1 ft(xt+1, xt) = ∑ i αi(xt+1)βi(xt).”
Parallel stochastic gradient algorithms for large-scale matrix completion (2013, background): “The other main regularizer of interest is the γ2-norm [16] (also known as the max-norm [22,36, 37]).”
Approximating the cut-norm via Grothendieck's inequality (2004, methodology): “The approach in this subsection is based on a simplified version of Krivine’s proof, presented in [18], of Grothendieck’s Inequality, as described in [15].”
Deterministic algorithms for matrix completion (2014, background): “Specifically, ν is the nuclear norm from 1 to ∞ [8].”