SimNano: A Trust Region Strategy for Large-Scale Molecular Systems Energy Minimization Based on Exact Second-Order Derivative Information

2018 ◽  
Vol 59 (1) ◽  
pp. 190-205 ◽  
Author(s):  
Stavros Chatzieleftheriou ◽  
Stefanos Anogiannakis ◽  
Doros N. Theodorou ◽  
Nikos D. Lagaros
Keyword(s):  
Energy Minimization ◽  
Large Scale ◽  
Trust Region ◽  
Second Order ◽  
Order Derivative ◽  
Molecular Systems ◽  
Derivative Information ◽  
Trust Region Strategy

Behavior Models of Voltage Differencing Inverting Buffered Amplifier and Applications in Circuit Analysis

2019 ◽  
Vol 12 (4) ◽  
pp. 298-303
Author(s):  
YongAn LI
Keyword(s):  
Large Scale ◽  
Vlsi Design ◽  
Circuit Analysis ◽  
Second Order ◽  
Very Large Scale Integration ◽  
Behavioral Models ◽  
Order Filter ◽  
Large Scale Integration ◽  
Scale Integration ◽  
Behavior Models

Background: The symbolic nodal analysis acts as a pivotal part of the very large scale integration (VLSI) design. Methods: In this work, based on the terminal relations for the pathological elements and the voltage differencing inverting buffered amplifier (VDIBA), twelve alternative pathological models for the VDIBA are presented. Moreover, the proposed models are applied to the VDIBA-based second-order filter and oscillator so as to simplify the circuit analysis. Results: The result shows that the behavioral models for the VDIBA are systematic, effective and powerful in the symbolic nodal circuit analysis.</P>

scholarly journals Plasma parameter profile inference from limited data utilizing second-order derivative priors and physic-based constraints

2021 ◽  
Vol 28 (3) ◽  
pp. 032504
Author(s):  
T. Nishizawa ◽  
M. Cavedon ◽  
R. Dux ◽  
F. Reimold ◽  
U. von Toussaint ◽  
...  
Keyword(s):  
Plasma Parameter ◽  
Second Order ◽  
Order Derivative ◽  
Limited Data ◽  
Parameter Profile

scholarly journals Higher order Hamiltonian Monte Carlo sampling for cosmological large-scale structure analysis

2021 ◽  
Vol 502 (3) ◽  
pp. 3976-3992
Author(s):  
Mónica Hernández-Sánchez ◽  
Francisco-Shu Kitaura ◽  
Metin Ata ◽  
Claudio Dalla Vecchia
Keyword(s):  
Fourth Order ◽  
Large Scale ◽  
Equations Of Motion ◽  
Large Scale Structure ◽  
Real Space ◽  
Higher Order ◽  
Second Order ◽  
Scale Structure ◽  
Integration Schemes ◽  
Reconstruction Methods

ABSTRACT We investigate higher order symplectic integration strategies within Bayesian cosmic density field reconstruction methods. In particular, we study the fourth-order discretization of Hamiltonian equations of motion (EoM). This is achieved by recursively applying the basic second-order leap-frog scheme (considering the single evaluation of the EoM) in a combination of even numbers of forward time integration steps with a single intermediate backward step. This largely reduces the number of evaluations and random gradient computations, as required in the usual second-order case for high-dimensional cases. We restrict this study to the lognormal-Poisson model, applied to a full volume halo catalogue in real space on a cubical mesh of 1250 h−1 Mpc side and 2563 cells. Hence, we neglect selection effects, redshift space distortions, and displacements. We note that those observational and cosmic evolution effects can be accounted for in subsequent Gibbs-sampling steps within the COSMIC BIRTH algorithm. We find that going from the usual second to fourth order in the leap-frog scheme shortens the burn-in phase by a factor of at least ∼30. This implies that 75–90 independent samples are obtained while the fastest second-order method converges. After convergence, the correlation lengths indicate an improvement factor of about 3.0 fewer gradient computations for meshes of 2563 cells. In the considered cosmological scenario, the traditional leap-frog scheme turns out to outperform higher order integration schemes only when considering lower dimensional problems, e.g. meshes with 643 cells. This gain in computational efficiency can help to go towards a full Bayesian analysis of the cosmological large-scale structure for upcoming galaxy surveys.

scholarly journals Meta-Descent for Online, Continual Prediction

2019 ◽  
Vol 33 ◽  
pp. 3943-3950
Author(s):  
Andrew Jacobsen ◽  
Matthew Schlegel ◽  
Cameron Linke ◽  
Thomas Degris ◽  
Adam White ◽  
...  
Keyword(s):  
Gradient Descent ◽  
Large Scale ◽  
Time Series Prediction ◽  
Real Data ◽  
Second Order ◽  
Stochastic Gradient Descent ◽  
Step Size ◽  
Vector Approximation ◽  
Prediction Problems ◽  
Stationary Problems

This paper investigates different vector step-size adaptation approaches for non-stationary online, continual prediction problems. Vanilla stochastic gradient descent can be considerably improved by scaling the update with a vector of appropriately chosen step-sizes. Many methods, including AdaGrad, RMSProp, and AMSGrad, keep statistics about the learning process to approximate a second order update—a vector approximation of the inverse Hessian. Another family of approaches use meta-gradient descent to adapt the stepsize parameters to minimize prediction error. These metadescent strategies are promising for non-stationary problems, but have not been as extensively explored as quasi-second order methods. We first derive a general, incremental metadescent algorithm, called AdaGain, designed to be applicable to a much broader range of algorithms, including those with semi-gradient updates or even those with accelerations, such as RMSProp. We provide an empirical comparison of methods from both families. We conclude that methods from both families can perform well, but in non-stationary prediction problems the meta-descent methods exhibit advantages. Our method is particularly robust across several prediction problems, and is competitive with the state-of-the-art method on a large-scale, time-series prediction problem on real data from a mobile robot.

scholarly journals Estimation of Tadalafil Using Derivative Spectrophotometry in Bulk Material and in Pharmaceutical Formulation

2014 ◽  
Vol 2014 ◽  
pp. 1-6 ◽  
Author(s):  
Zamir G. Khan ◽  
Amod S. Patil ◽  
Atul A. Shirkhedkar
Keyword(s):  
Wavelength Range ◽  
Area Under Curve ◽  
Pharmaceutical Formulation ◽  
Second Order ◽  
Order Derivative ◽  
Uv Spectrophotometry ◽  
First Order ◽  
Beer's Law ◽  
Beer’S Law ◽  
Derivative Uv Spectrophotometry

Four simple, rapid, accurate, precise, reliable, and economical UV-spectrophotometric methods have been proposed for the determination of tadalafil in bulk and in pharmaceutical formulation. “Method A” is first order derivative UV spectrophotometry using amplitude, “method B” is first order derivative UV spectrophotometry using area under curve technique, “method C” is second order derivative UV spectrophotometry using amplitude, and “method D” is second order derivative UV spectrophotometry using area under curve technique. The developed methods have shown best results in terms of linearity, accuracy, precision, and LOD and LOQ for bulk drug and marketed formulation as well. In N,N-dimethylformamide, tadalafil showed maximum absorbance at 284 nm. For “method A” amplitude was recorded at 297 nm while for “method B” area under curve was integrated in the wavelength range of 290.60–304.40 nm. For “method C” amplitude was measured at 284 nm while for “method D” area under curve was selected in the wavelength range of 280.80–286.20 nm. For methods A and B, tadalafil obeyed Lambert-Beer’s law in the range of 05–50 μg/mL while for “methods C and D”, tadalafil obeyed Lambert-Beer’s law in the range of 20–70 μg/mL, and-for “methods A, B, C, and D” the correlation coefficients were found to be > than 0.999.

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