scholarly journals Distributed Learning Systems with First-Order Methods

2020 ◽  
Vol 9 (1) ◽  
pp. 1-100
Author(s):  
Ji Liu ◽  
Ce Zhang
Keyword(s):  
Distributed Learning ◽  
Learning Systems ◽  
First Order ◽  
First Order Methods

Assessing Medical Evidence

2018 ◽  
Author(s):  
Jacob Stegenga
Keyword(s):  
Quality Assessment ◽  
Randomized Trials ◽  
Empirical Studies ◽  
Allocation Concealment ◽  
Assessment Tools ◽  
Medical Evidence ◽  
Empirical Methods ◽  
First Order ◽  
First Order Methods

Medical scientists employ ‘quality assessment tools’ to assess evidence from medical research, especially from randomized trials. These tools are designed to take into account methodological details of studies, including randomization, subject allocation concealment, and other features of studies deemed relevant to minimizing bias. There are dozens of such tools available. They differ widely from each other, and empirical studies show that they have low inter-rater reliability and low inter-tool reliability. This is an instance of a more general problem called here the underdetermination of evidential significance. Disagreements about the quality of evidence can be due to different—but in principle equally good—weightings of the methodological features that constitute quality assessment tools. Thus, the malleability of empirical research in medicine is deep: in addition to the malleability of first-order empirical methods, such as randomized trials, there is malleability in the tools used to evaluate first-order methods.

scholarly journals A General Framework for Decentralized Optimization With First-Order Methods

2020 ◽  
Vol 108 (11) ◽  
pp. 1869-1889
Author(s):  
Ran Xin ◽  
Shi Pu ◽  
Angelia Nedic ◽  
Usman A. Khan
Keyword(s):  
General Framework ◽  
Decentralized Optimization ◽  
First Order ◽  
First Order Methods

First-order methods of smooth convex optimization with inexact oracle

2013 ◽  
Vol 146 (1-2) ◽  
pp. 37-75 ◽  
Author(s):  
Olivier Devolder ◽  
François Glineur ◽  
Yurii Nesterov
Keyword(s):  
Convex Optimization ◽  
Smooth Convex ◽  
First Order ◽  
Inexact Oracle ◽  
Smooth Convex Optimization ◽  
First Order Methods

scholarly journals Stabilised explicit Adams-type methods

2021 ◽  
pp. 82-98
Author(s):  
Vasily I. Repnikov ◽  
Boris V. Faleichik ◽  
Andrew V. Moisa
Keyword(s):  
Numerical Experiments ◽  
Step Method ◽  
Type Method ◽  
Constrained Optimisation ◽  
First Order ◽  
Error Constant ◽  
Stability Intervals ◽  
Multi Step Methods ◽  
First Order Methods ◽  
Accuracy And Stability

In this work we present explicit Adams-type multi-step methods with extended stability intervals, which are analogous to the stabilised Chebyshev Runge – Kutta methods. It is proved that for any k ≥ 1 there exists an explicit k-step Adams-type method of order one with stability interval of length 2k. The first order methods have remarkably simple expressions for their coefficients and error constant. A damped modification of these methods is derived. In the general case, to construct a k-step method of order p it is necessary to solve a constrained optimisation problem in which the objective function and p constraints are second degree polynomials in k variables. We calculate higher-order methods up to order six numerically and perform some numerical experiments to confirm the accuracy and stability of the methods.

scholarly journals First-Order Methods for Convex Optimization

2021 ◽  
pp. 100015
Author(s):  
Pavel Dvurechensky ◽  
Shimrit Shtern ◽  
Mathias Staudigl
Keyword(s):  
Convex Optimization ◽  
First Order ◽  
First Order Methods

scholarly journals FIRST-ORDER METHODS FOR GENERALIZED OPTIMAL CONTROL PROBLEMS FOR SYSTEMS WITH DISTRIBUTED PARAMETERS

2020 ◽  
pp. 18-44
Author(s):  
S. V. Denisov ◽  
V. V. Semenov
Keyword(s):  
Optimal Control Problems ◽  
A Priori Estimates ◽  
A Priori ◽  
Control Problems ◽  
First Order ◽  
Admissible Sets ◽  
Distributed Parameters ◽  
Priori Estimates ◽  
Systems With Distributed Parameters ◽  
First Order Methods

The problems of optimization of linear distributed systems with generalized control and first-order methods for their solution are considered. The main focus is on proving the convergence of methods. It is assumed that the operator describing the model satisfies a priori estimates in negative norms. For control problems with convex and preconvex admissible sets, the convergence of several first-order algorithms with errors in iterative subproblems is proved.

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