无法在具有特定乳胶类别的科学期刊中添加作者的所属关系

Question

尝试此代码

\documentclass[times]{iapress}
\usepackage{moreverb}

%\usepackage[dvips,colorlinks,bookmarksopen,bookmarksnumbered,citecolor=red,urlcolor=red]{hyperref} % if run by LaTex (Shift + Ctrl + L)
\usepackage[colorlinks,bookmarksopen,bookmarksnumbered,citecolor=red,urlcolor=red]{hyperref} % if run by PDFLaTex


\def\volumeyear{202x}
\def\volumenumber{x}
\def\volumemonth{Month}
\setcounter{page}{00}

\usepackage{amsmath,amssymb}
\usepackage{graphicx}
\renewcommand{\baselinestretch}{1.01}   

\usepackage{colortbl}
\usepackage[margin=1cm]{caption}
\usepackage{multirow}
\usepackage{booktabs}
\usepackage{float}
\usepackage{graphicx}
\graphicspath{{figures/}}

\usepackage{longtable}

%\usepackage{algorithm}

\usepackage{algorithmic}

\usepackage{pdfpages}
\usepackage[ ruled,vlined]{algorithm2e}
\usepackage{ifoddpage}
\usepackage{blindtext}
%\usepackage{authblk} 
\usepackage{listings}
\usepackage{xcolor}
\usepackage{ragged2e}
\usepackage{lipsum}

%% added for affiliations   

%%
\makeatletter
\def\normaljustify{%
  \let\\\@centercr\rightskip\z@skip \leftskip\z@skip%
  \parfillskip=0pt plus 1fil}
\makeatother


%% attention here ! 
\renewcommand{\topfraction}{0.9}

\lstset{
basicstyle=\scriptsize\tt,
}
%%
\definecolor{codegreen}{rgb}{0,0.6,0}
\definecolor{codegray}{rgb}{0.5,0.5,0.5}
\definecolor{codepurple}{rgb}{0.58,0,0.82}
\definecolor{backcolour}{rgb}{0.95,0.95,0.92}

\lstdefinestyle{mystyle}{
    backgroundcolor=\color{backcolour},   
    commentstyle=\color{codegreen},
    keywordstyle=\color{magenta},
    numberstyle=\tiny\color{codegray},
    stringstyle=\color{codepurple},
    basicstyle=\ttfamily\footnotesize,
    breakatwhitespace=false,         
    breaklines=true,                 
    captionpos=b,                    
    keepspaces=true,                 
    numbers=left,                    
    numbersep=5pt,                  
    showspaces=false,                
    showstringspaces=false,
    showtabs=false,                  
    tabsize=2
}

%   

\usepackage{pdflscape}  

%\renewenvironment{abstract}
%{\par\noindent\textbf{\abstractname.}\ \ignorespaces}
%{\par\medskip}
    
\begin{document}

\runningheads{Image reconstruction from incomplete convolution data}{Z. Shen, Z. Geng and J. Yang}

\title{Image reconstruction from incomplete convolution data via total variation regularization}
%\footnote[2]{This
%work was partly supported by Chinese NSF grant (NO. 10771162)}}

\author{Zhida Shen \affil{1},
    Zhe Geng \affil{2}, Junfeng Yang \affil{1}$^,$\corrauth
}

\address{
    \affilnum{1}Department of Mathematics, Nanjing University, China;
    \affilnum{2}Department of Mathematics, Peiking University, China
}

\corraddr{Junfeng Yang (Email: [email protected]). Department of Mathematics, Nanjing University. 22 Hankou Road, Nanjing, Jiangsu Province, China (210093).}

    

\begin{abstract}
\small
In this paper, we present an empirical comparison of some Gradient Descent variants used to solve global optimization problems for large search domains. The aim is to identify which one of them is more suitable for solving an optimization problem regardless of the features of the used test function. Five variants of Gradient Descent were implemented in the R language and tested on a benchmark of five test functions. We proved the dependence between the choice of the variant and the obtained performances using the khi-2 test in a sample of 120 experiments. Those test functions vary on convexity, the number of local minima, and are classified according to some criteria. We had chosen a range of values for each algorithm parameter. Results are compared in terms of accuracy and convergence speed.  Based on the obtained results, we defined the priority of usage for those variants and we contributed by a new hybrid optimizer. The new optimizer is tested in a benchmark of well-known test functions and two real applications are proposed. Except for the classical gradient descent algorithm, only stochastic versions of those variants are considered in this paper.
\end{abstract}



\keywords{Keywords: global numerical optimization, mono-objective, descent gradient variants, analytic hierarchy process, hybrid optimization, random search}

\maketitle

\section{Introduction}

\small
Optimization techniques have common applications in fields such as differential calculus, regression models for prediction, shape optimization, topological optimization, and other applications in logistic and graph theory \cite{yang2018optimization}.
The optimization is mono-objective when it consists of finding the best solution that optimizes a given objective \cite{kelley1999iterative}. On the other hand, multi-objective optimization concerns multiple contradictory criteria for making a decision \cite{cavazzuti2012optimization}.
Commonly, Numerical methods can provide practical and adaptable solutions for both cases. Although finding exact analytical solutions is a hard task because of dimensions or because of the nature of the objective function, algorithms such as gradient descent are considered to find acceptable solutions with an error margin \cite{grivet2012methodes}.
One of the main issues with gradient descent variants is how to select the appropriate algorithm according to the problem’s features. When it comes to applying gradient descent variants on a real application, a practitioner will prefer to use some criteria for making a quick decision. Because not all variants have the same performance. The use of a decision technique will help in saving time, especially while performing a simulation. For this, we will compare the performance of gradient descent variants based on a panel of test functions. After that, we will apply a khi-2 test to help in deploying suitable decisions that match the researcher’s goals or understanding of a problem.
The paper is organized as follows: In Section 2, we provide a review of the related work.
%\bibliographystyle{ieeetr}
%\bibliography{references.bib}

\end{document}

Answer 1

尝试此代码

\documentclass[times]{iapress}
\usepackage{moreverb}

%\usepackage[dvips,colorlinks,bookmarksopen,bookmarksnumbered,citecolor=red,urlcolor=red]{hyperref} % if run by LaTex (Shift + Ctrl + L)
\usepackage[colorlinks,bookmarksopen,bookmarksnumbered,citecolor=red,urlcolor=red]{hyperref} % if run by PDFLaTex


\def\volumeyear{202x}
\def\volumenumber{x}
\def\volumemonth{Month}
\setcounter{page}{00}

\usepackage{amsmath,amssymb}
\usepackage{graphicx}
\renewcommand{\baselinestretch}{1.01}   

\usepackage{colortbl}
\usepackage[margin=1cm]{caption}
\usepackage{multirow}
\usepackage{booktabs}
\usepackage{float}
\usepackage{graphicx}
\graphicspath{{figures/}}

\usepackage{longtable}

%\usepackage{algorithm}

\usepackage{algorithmic}

\usepackage{pdfpages}
\usepackage[ ruled,vlined]{algorithm2e}
\usepackage{ifoddpage}
\usepackage{blindtext}
%\usepackage{authblk} 
\usepackage{listings}
\usepackage{xcolor}
\usepackage{ragged2e}
\usepackage{lipsum}

%% added for affiliations   

%%
\makeatletter
\def\normaljustify{%
  \let\\\@centercr\rightskip\z@skip \leftskip\z@skip%
  \parfillskip=0pt plus 1fil}
\makeatother


%% attention here ! 
\renewcommand{\topfraction}{0.9}

\lstset{
basicstyle=\scriptsize\tt,
}
%%
\definecolor{codegreen}{rgb}{0,0.6,0}
\definecolor{codegray}{rgb}{0.5,0.5,0.5}
\definecolor{codepurple}{rgb}{0.58,0,0.82}
\definecolor{backcolour}{rgb}{0.95,0.95,0.92}

\lstdefinestyle{mystyle}{
    backgroundcolor=\color{backcolour},   
    commentstyle=\color{codegreen},
    keywordstyle=\color{magenta},
    numberstyle=\tiny\color{codegray},
    stringstyle=\color{codepurple},
    basicstyle=\ttfamily\footnotesize,
    breakatwhitespace=false,         
    breaklines=true,                 
    captionpos=b,                    
    keepspaces=true,                 
    numbers=left,                    
    numbersep=5pt,                  
    showspaces=false,                
    showstringspaces=false,
    showtabs=false,                  
    tabsize=2
}

%   

\usepackage{pdflscape}  

%\renewenvironment{abstract}
%{\par\noindent\textbf{\abstractname.}\ \ignorespaces}
%{\par\medskip}
    
\begin{document}

\runningheads{Image reconstruction from incomplete convolution data}{Z. Shen, Z. Geng and J. Yang}

\title{Image reconstruction from incomplete convolution data via total variation regularization}
%\footnote[2]{This
%work was partly supported by Chinese NSF grant (NO. 10771162)}}

\author{Zhida Shen \affil{1},
    Zhe Geng \affil{2}, Junfeng Yang \affil{1}$^,$\corrauth
}

\address{
    \affilnum{1}Department of Mathematics, Nanjing University, China;
    \affilnum{2}Department of Mathematics, Peiking University, China
}

\corraddr{Junfeng Yang (Email: [email protected]). Department of Mathematics, Nanjing University. 22 Hankou Road, Nanjing, Jiangsu Province, China (210093).}

    

\begin{abstract}
\small
In this paper, we present an empirical comparison of some Gradient Descent variants used to solve global optimization problems for large search domains. The aim is to identify which one of them is more suitable for solving an optimization problem regardless of the features of the used test function. Five variants of Gradient Descent were implemented in the R language and tested on a benchmark of five test functions. We proved the dependence between the choice of the variant and the obtained performances using the khi-2 test in a sample of 120 experiments. Those test functions vary on convexity, the number of local minima, and are classified according to some criteria. We had chosen a range of values for each algorithm parameter. Results are compared in terms of accuracy and convergence speed.  Based on the obtained results, we defined the priority of usage for those variants and we contributed by a new hybrid optimizer. The new optimizer is tested in a benchmark of well-known test functions and two real applications are proposed. Except for the classical gradient descent algorithm, only stochastic versions of those variants are considered in this paper.
\end{abstract}



\keywords{Keywords: global numerical optimization, mono-objective, descent gradient variants, analytic hierarchy process, hybrid optimization, random search}

\maketitle

\section{Introduction}

\small
Optimization techniques have common applications in fields such as differential calculus, regression models for prediction, shape optimization, topological optimization, and other applications in logistic and graph theory \cite{yang2018optimization}.
The optimization is mono-objective when it consists of finding the best solution that optimizes a given objective \cite{kelley1999iterative}. On the other hand, multi-objective optimization concerns multiple contradictory criteria for making a decision \cite{cavazzuti2012optimization}.
Commonly, Numerical methods can provide practical and adaptable solutions for both cases. Although finding exact analytical solutions is a hard task because of dimensions or because of the nature of the objective function, algorithms such as gradient descent are considered to find acceptable solutions with an error margin \cite{grivet2012methodes}.
One of the main issues with gradient descent variants is how to select the appropriate algorithm according to the problem’s features. When it comes to applying gradient descent variants on a real application, a practitioner will prefer to use some criteria for making a quick decision. Because not all variants have the same performance. The use of a decision technique will help in saving time, especially while performing a simulation. For this, we will compare the performance of gradient descent variants based on a panel of test functions. After that, we will apply a khi-2 test to help in deploying suitable decisions that match the researcher’s goals or understanding of a problem.
The paper is organized as follows: In Section 2, we provide a review of the related work.
%\bibliographystyle{ieeetr}
%\bibliography{references.bib}

\end{document}

无法在具有特定乳胶类别的科学期刊中添加作者的所属关系

答案1

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