matrix products
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Author(s):  
Aurél Galántai

AbstractWe develop a matrix form of the Nelder-Mead simplex method and show that its convergence is related to the convergence of infinite matrix products. We then characterize the spectra of the involved matrices necessary for the study of convergence. Using these results, we discuss several examples of possible convergence or failure modes. Then, we prove a general convergence theorem for the simplex sequences generated by the method. The key assumption of the convergence theorem is proved in low-dimensional spaces up to 8 dimensions.


Bernoulli ◽  
2021 ◽  
Vol 27 (4) ◽  
Author(s):  
Rohit Chaudhuri ◽  
Vishesh Jain ◽  
Natesh S. Pillai

2021 ◽  
Vol 11 (4) ◽  
pp. 5671-5679
Author(s):  
Luara Batalha Vieira ◽  
Giulia Costa Ghirardi ◽  
Josiane Dantas Viana ◽  
Larissa Da Silva Paes Cardoso ◽  
Cleber Marcos Ribeiro Dias

The civil construction industry affects the environment mainly due to the high generation of waste and extraction of natural raw materials. However, this industry is also responsible for absorbing a large part of the waste, not only generated by itself, but also those generated in other production cycles. These residues are often reused as an alternative raw material, after some treatment. The use of ornamental stone cutting waste has already been studied by many authors, and it is a technically viable alternative in cement matrix products for civil construction. Thus, this technological prospection work analyzed the technological trends regarding the use of marble waste in cement production, based on the registration of patents in recent years. For this, a search was carried out in the Brazilian Nacional Institute of Intellectual Property (INPI) and Dewert Innovation Index (DII) databases, in April 2021, with no period limitation. The main documents were found in the DII database, and the results showed that among the countries that filed a patent on this subject, China is the country with the highest number of filings, with the majority of these filings occurring between 2014 and 2020, suggesting a reflection of the current interest in ways to give an adequate destination to ornamental stone waste.


Mathematics ◽  
2021 ◽  
Vol 9 (14) ◽  
pp. 1600
Author(s):  
Jorge Sastre ◽  
Javier Ibáñez

Recently, two general methods for evaluating matrix polynomials requiring one matrix product less than the Paterson–Stockmeyer method were proposed, where the cost of evaluating a matrix polynomial is given asymptotically by the total number of matrix product evaluations. An analysis of the stability of those methods was given and the methods have been applied to Taylor-based implementations for computing the exponential, the cosine and the hyperbolic tangent matrix functions. Moreover, a particular example for the evaluation of the matrix exponential Taylor approximation of degree 15 requiring four matrix products was given, whereas the maximum polynomial degree available using Paterson–Stockmeyer method with four matrix products is 9. Based on this example, a new family of methods for evaluating matrix polynomials more efficiently than the Paterson–Stockmeyer method was proposed, having the potential to achieve a much higher efficiency, i.e., requiring less matrix products for evaluating a matrix polynomial of certain degree, or increasing the available degree for the same cost. However, the difficulty of these family of methods lies in the calculation of the coefficients involved for the evaluation of general matrix polynomials and approximations. In this paper, we provide a general matrix polynomial evaluation method for evaluating matrix polynomials requiring two matrix products less than the Paterson-Stockmeyer method for degrees higher than 30. Moreover, we provide general methods for evaluating matrix polynomial approximations of degrees 15 and 21 with four and five matrix product evaluations, respectively, whereas the maximum available degrees for the same cost with the Paterson–Stockmeyer method are 9 and 12, respectively. Finally, practical examples for evaluating Taylor approximations of the matrix cosine and the matrix logarithm accurately and efficiently with these new methods are given.


Mathematics ◽  
2021 ◽  
Vol 9 (11) ◽  
pp. 1219
Author(s):  
Javier Ibáñez ◽  
José M. Alonso ◽  
Jorge Sastre ◽  
Emilio Defez ◽  
Pedro Alonso-Jordá

In this paper, we introduce two approaches to compute the matrix hyperbolic tangent. While one of them is based on its own definition and uses the matrix exponential, the other one is focused on the expansion of its Taylor series. For this second approximation, we analyse two different alternatives to evaluate the corresponding matrix polynomials. This resulted in three stable and accurate codes, which we implemented in MATLAB and numerically and computationally compared by means of a battery of tests composed of distinct state-of-the-art matrices. Our results show that the Taylor series-based methods were more accurate, although somewhat more computationally expensive, compared with the approach based on the exponential matrix. To avoid this drawback, we propose the use of a set of formulas that allows us to evaluate polynomials in a more efficient way compared with that of the traditional Paterson–Stockmeyer method, thus, substantially reducing the number of matrix products (practically equal in number to the approach based on the matrix exponential), without penalising the accuracy of the result.


2021 ◽  
Vol 127 (1) ◽  
pp. 43-62
Author(s):  
Kenta Endo ◽  
Ippei Mimura ◽  
Yusuke Sawada

Wildberger's construction enables us to obtain a hypergroup from a random walk on a special graph. We will give a probability theoretic interpretation to products on the hypergroup. The hypergroup can be identified with a commutative algebra whose basis is transition matrices. We will estimate the operator norm of such a transition matrix and clarify a relationship between their matrix products and random walks.


2021 ◽  
Vol Publish Ahead of Print ◽  
Author(s):  
Michael Downing ◽  
Timothy Niedzielak ◽  
Charles De La Rosa ◽  
Alexander Ting ◽  
Joshua Berko ◽  
...  

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