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This paper introduces AdaSDCA: an adaptive variant of stochastic dual coordinate ascent (SDCA) for solving the regularized empirical risk minimization problems. Our modification consists in allowing the method adaptively change the probability distribution over the dual variables throughout the iterative process. AdaSDCA achieves provably better complexity(More)
We study the problem of minimizing the average of a large number of smooth convex functions penalized with a strongly convex regularizer. We propose and analyze a novel primal-dual method (Quartz) which at every iteration samples and updates a random subset of the dual variables, chosen according to an arbitrary distribution. In contrast to typical(More)
We propose a new algorithm for minimizing regularized empirical loss: Stochastic Dual Newton Ascent (SDNA). Our method is dual in nature: in each iteration we update a random subset of the dual variables. However, unlike existing methods such as stochastic dual coordinate ascent, SDNA is capable of utilizing all local curvature information contained in the(More)
Layer-by-layer assembly of polyelectrolyte multilayer (PEM) films represents a bottom-up approach for re-engineering the molecular landscape of cell surfaces with spatially continuous and molecularly uniform ultrathin films. However, fabricating PEMs on viable cells has proven challenging owing to the high cytotoxicity of polycations. Here, we report the(More)
We study the problem of minimizing the average of a large number of smooth convex functions penalized with a strongly convex regularizer. We propose and analyze a novel primal-dual method (Quartz) which at every iteration samples and updates a random subset of the dual variables, chosen according to an arbitrary distribution. In contrast to typical(More)
Since wireless sensor network has limited resources, it’s important to design its task allocation algorithm reasonably to reduce energy consumption. The contract net is simple and flexible so that it can meet the needs of the wireless sensor network. In this paper, we introduce the improved C-MEANS algorithm to cluster nodes to decrease the number of(More)
Inherited cardiomyopathies are caused by point mutations in sarcomeric gene products, including α-cardiac muscle actin (ACTC1). We examined the biochemical and cell biological properties of the α-cardiac actin mutations Y166C and M305L identified in hypertrophic cardiomyopathy (HCM). Untagged wild-type (WT) cardiac actin, and the Y166C and M305L mutants(More)
We propose an efficient distributed randomized coordinate descent method for minimizing regularized non-strongly convex loss functions. The method attains the optimal O(1/k<sup>2</sup>) convergence rate, where k is the iteration counter. The core of the work is the theoretical study of stepsize parameters. We have implemented the method on Archer - the(More)
We study the problem of minimizing the sum of a smooth convex function and a convex blockseparable regularizer and propose a new randomized coordinate descent method, which we call ALPHA. Our method at every iteration updates a random subset of coordinates, following an arbitrary distribution. No coordinate descent methods capable to handle an arbitrary(More)