نتایج جستجو برای: co prox
تعداد نتایج: 333412 فیلتر نتایج به سال:
BACKGROUND To identify differences between proximal and distal cell rewiring with subsequent kissing balloon inflation (KBI) in the presence of a link connected to a carina on the jailed side branch ostium (SBO). METHODS Kaname stents were deployed in bifurcation models (n = 12) with subsequent KBI and were confirmed by optical coherence tomography. The jailing configuration and cell rewiring...
The molecular basis of lymphangiogenesis remains incompletely characterized. Here, we document a novel role for the PDZ domain-containing scaffold protein synectin in lymphangiogenesis using genetic studies in zebrafish and tadpoles. In zebrafish, the thoracic duct arises from parachordal lymphangioblast cells, which in turn derive from secondary lymphangiogenic sprouts from the posterior cardi...
In the paper, we develop a composite version of Mirror Prox algorithm for solving convexconcave saddle point problems and monotone variational inequalities of special structure, allowing to cover saddle point/variational analogies of what is usually called “composite minimization” (minimizing a sum of an easy-to-handle nonsmooth and a general-type smooth convex functions “as if” there were no n...
It is known, by Rockafellar (SIAM J Control Optim 14:877–898, 1976), that the proximal point algorithm (PPA) converges weakly to a zero of a maximal monotone operator in a Hilbert space, but it fails to converge strongly. Lehdili and Moudafi (Optimization 37:239–252, 1996) introduced the new prox-Tikhonov regularization method for PPA to generate a strongly convergent sequence and established a...
We propose a new first-order optimisation algorithm to solve high-dimensional non-smooth composite minimisation problems. Typical examples of such problems have an objective that decomposes into a non-smooth empirical risk part and a non-smooth regularisation penalty. The proposed algorithm, called Semi-Proximal Mirror-Prox, leverages the Fenchel-type representation of one part of the objective...
We derive new prox-functions on the simplex from additive random utility models of discrete choice. They are convex conjugates corresponding surplus functions. In particular, we explicitly convexity parameter choice associated with generalized extreme value models, and specifically nested logit models. Incorporated into subgradient schemes, lead to a probabilistic interpretations iteration step...
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