Bounds on characteristics of algorithms to find the global minimum of a function in anN-dimensional parallelepiped with prespecified accuracy

1996 ◽  
Vol 32 (3) ◽  
pp. 424-428
Author(s):  
V. A. Lyudvichenko
Keyword(s):  
Global Minimum ◽  
Prespecified Accuracy

Properties of the beta coefficient of the global minimum variance portfolio

2020 ◽  
Vol 8 (1) ◽  
pp. 11-21
Author(s):  
S. M. Yaroshko ◽  
◽  
M. V. Zabolotskyy ◽  
T. M. Zabolotskyy ◽  
◽  
...  
Keyword(s):  
Confidence Interval ◽  
Asymptotic Distribution ◽  
Global Minimum ◽  
Asset Returns ◽  
Minimum Variance ◽  
Benchmark Portfolio ◽  
Beta Coefficient ◽  
Minimum Variance Portfolio ◽  
Global Minimum Variance Portfolio ◽  
Daily Returns

The paper is devoted to the investigation of statistical properties of the sample estimator of the beta coefficient in the case when the weights of benchmark portfolio are constant and for the target portfolio, the global minimum variance portfolio is taken. We provide the asymptotic distribution of the sample estimator of the beta coefficient assuming that the asset returns are multivariate normally distributed. Based on the asymptotic distribution we construct the confidence interval for the beta coefficient. We use the daily returns on the assets included in the DAX index for the period from 01.01.2018 to 30.09.2019 to compare empirical and asymptotic means, variances and densities of the standardized estimator for the beta coefficient. We obtain that the bias of the sample estimator converges to zero very slowly for a large number of assets in the portfolio. We present the adjusted estimator of the beta coefficient for which convergence of the empirical variances to the asymptotic ones is not significantly slower than for a sample estimator but the bias of the adjusted estimator is significantly smaller.

Researching nitrogen solubility in nitrogen-containing austenitic steels at melting and recrystallization by CALPHAD method

2019 ◽  
pp. 53-66 ◽  
Author(s):  
L. A. Smirnov ◽  
I. I. Gorbachev ◽  
V. V. Popov ◽  
A. Yu. Pasynkov ◽  
A. S. Oryshchenko ◽  
...  
Keyword(s):  
Global Minimum ◽  
Nitrogen Solubility ◽  
Thermodynamic Description ◽  
Solidus Temperature ◽  
Liquidus Line ◽  
Calphad Method ◽  
Austenitic Steels ◽  
Nitrogen Transfer ◽  
Fixed Nitrogen ◽  
System Phase

The CALPHAD method has been employed to compose thermodynamic description of the Fe–Cr–Mn–Ni–Si–C–N system. Using an algorithm based on finding a global minimum of Gibbs energy, the calculations of system phase composition were performed in the temperature range from 1750°C to hardening and in the range of compositions corresponding to 04Kh20N6G11M2AFB steel. Calculations showed that at temperatures above liquidus line, Cr and Mn increase nitrogen solubility in the melt, while Ni and Si reduce it. With an increase in the content of Cr, Mn, Ni, and Si in steel in the studied composition range, both liquidus and solidus temperature decrease. The degree of influence on these temperatures of Cr, Mn, Ni and Si within the steel grade is different and ranges from ~3 to ~14°C. Calculations taking into account the possibility of nitrogen transfer between steel and the atmosphere of air showed that the amount of fixed nitrogen in the alloy under study varies, depending on the composition of the steel and temperature, from ~0.3 to ~0.6 wt%. As the temperature decreases from liquidus to solidus, the amount of fixed nitrogen increases, with the exception of those steel compositions when ferrite and not austenite is released from the liquid phase.

scholarly journals Adding Prior Information in FWI through Relative Entropy

Entropy ◽  
2021 ◽  
Vol 23 (5) ◽  
pp. 599
Author(s):  
Danilo Cruz ◽  
João de Araújo ◽  
Carlos da Costa ◽  
Carlos da Silva
Keyword(s):  
Inverse Problem ◽  
Relative Entropy ◽  
Global Minimum ◽  
Prior Information ◽  
Waveform Inversion ◽  
Petroleum Industry ◽  
Synthetic Data ◽  
Full Waveform Inversion ◽  
Full Waveform

Full waveform inversion is an advantageous technique for obtaining high-resolution subsurface information. In the petroleum industry, mainly in reservoir characterisation, it is common to use information from wells as previous information to decrease the ambiguity of the obtained results. For this, we propose adding a relative entropy term to the formalism of the full waveform inversion. In this context, entropy will be just a nomenclature for regularisation and will have the role of helping the converge to the global minimum. The application of entropy in inverse problems usually involves formulating the problem, so that it is possible to use statistical concepts. To avoid this step, we propose a deterministic application to the full waveform inversion. We will discuss some aspects of relative entropy and show three different ways of using them to add prior information through entropy in the inverse problem. We use a dynamic weighting scheme to add prior information through entropy. The idea is that the prior information can help to find the path of the global minimum at the beginning of the inversion process. In all cases, the prior information can be incorporated very quickly into the full waveform inversion and lead the inversion to the desired solution. When we include the logarithmic weighting that constitutes entropy to the inverse problem, we will suppress the low-intensity ripples and sharpen the point events. Thus, the addition of entropy relative to full waveform inversion can provide a result with better resolution. In regions where salt is present in the BP 2004 model, we obtained a significant improvement by adding prior information through the relative entropy for synthetic data. We will show that the prior information added through entropy in full-waveform inversion formalism will prove to be a way to avoid local minimums.

scholarly journals An Adaptive Optimization Method Based on Learning Rate Schedule for Neural Networks

2021 ◽  
Vol 11 (2) ◽  
pp. 850
Author(s):  
Dokkyun Yi ◽  
Sangmin Ji ◽  
Jieun Park
Keyword(s):  
Artificial Intelligence ◽  
Cost Function ◽  
Numerical Experiments ◽  
Global Minimum ◽  
Optimization Method ◽  
Learning Method ◽  
Adaptive Optimization ◽  
The Cost ◽  
Proof Of Convergence ◽  
Learning Data

Artificial intelligence (AI) is achieved by optimizing the cost function constructed from learning data. Changing the parameters in the cost function is an AI learning process (or AI learning for convenience). If AI learning is well performed, then the value of the cost function is the global minimum. In order to obtain the well-learned AI learning, the parameter should be no change in the value of the cost function at the global minimum. One useful optimization method is the momentum method; however, the momentum method has difficulty stopping the parameter when the value of the cost function satisfies the global minimum (non-stop problem). The proposed method is based on the momentum method. In order to solve the non-stop problem of the momentum method, we use the value of the cost function to our method. Therefore, as the learning method processes, the mechanism in our method reduces the amount of change in the parameter by the effect of the value of the cost function. We verified the method through proof of convergence and numerical experiments with existing methods to ensure that the learning works well.

scholarly journals Exploration of Free Energy Surface and Thermal Effects on Relative Population and Infrared Spectrum of the Be6B11− Fluxional Cluster

Materials ◽  
2020 ◽  
Vol 14 (1) ◽  
pp. 112
Author(s):  
Carlos Emiliano Buelna-Garcia ◽  
José Luis Cabellos ◽  
Jesus Manuel Quiroz-Castillo ◽  
Gerardo Martinez-Guajardo ◽  
Cesar Castillo-Quevedo ◽  
...  
Keyword(s):  
Free Energy ◽  
Infrared Spectrum ◽  
Infrared Spectra ◽  
Thermal Effects ◽  
Global Minimum ◽  
Energy Surface ◽  
Weighted Sums ◽  
Free Energy Surface ◽  
Temperature Dependent ◽  
Relative Population

The starting point to understanding cluster properties is the putative global minimum and all the nearby local energy minima; however, locating them is computationally expensive and difficult. The relative populations and spectroscopic properties that are a function of temperature can be approximately computed by employing statistical thermodynamics. Here, we investigate entropy-driven isomers distribution on Be6B11− clusters and the effect of temperature on their infrared spectroscopy and relative populations. We identify the vibration modes possessed by the cluster that significantly contribute to the zero-point energy. A couple of steps are considered for computing the temperature-dependent relative population: First, using a genetic algorithm coupled to density functional theory, we performed an extensive and systematic exploration of the potential/free energy surface of Be6B11− clusters to locate the putative global minimum and elucidate the low-energy structures. Second, the relative populations’ temperature effects are determined by considering the thermodynamic properties and Boltzmann factors. The temperature-dependent relative populations show that the entropies and temperature are essential for determining the global minimum. We compute the temperature-dependent total infrared spectra employing the Boltzmann factor weighted sums of each isomer’s infrared spectrum and find that at finite temperature, the total infrared spectrum is composed of an admixture of infrared spectra that corresponds to the spectra of the lowest-energy structure and its isomers located at higher energies. The methodology and results describe the thermal effects in the relative population and the infrared spectra.

Multicriteria Global Minimum Cuts

Algorithmica ◽  
2006 ◽  
Vol 46 (1) ◽  
pp. 15-26 ◽  
Author(s):  
Amitai Armon ◽  
Uri Zwick
Keyword(s):  
Global Minimum ◽  
Minimum Cuts

The [12]Annulene Global Minimum

2008 ◽  
Vol 73 (4) ◽  
pp. 1532-1535 ◽  
Author(s):  
Miles N. Braten ◽  
Claire Castro ◽  
Rainer Herges ◽  
Felix Köhler ◽  
William L. Karney
Keyword(s):  
Global Minimum
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