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\begin{document}
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\title{Regularization and Optimization is All You Need?}

\author{Lars Bogner}
\affiliation{\url{https://github.uio.no/larsbog/FYSSTK-Project1}}


\begin{abstract}
  The basis for many data-driven methods is the optimization of numerical models to describe relations in data. With the increasing availability of large datasets and the growing complexity of models, there is an increasing need for efficient optimization techniques that can handle the computational challenges associated with big data. Furthermore, a good optimization metric can be crucial to find the best possible model to describe the data. In this paper different methods of such optimization are studied in the context of large datasets and big data with possibly noise-affected input data. To render the best possible fits a multitude of cost functions, minimization algorithms and other approaches to reduce the computation associated with the optimization process are studied and evaluated. Reducing the computational cost is also important to improve the economical and ecological footprint of training large models on big data, as the spread of datadriven methods in all areas of life is increasing rapidly.
  Using optimal optimization techniques the convergence can be improved by up to one order of magnitude.
\end{abstract}

\maketitle

\tableofcontents


\section{Introduction} \label{sec:introduction}
\input{chapters/introduction.tex}


\section{Methods} \label{sec:methods}
\input{chapters/methods.tex}

\section{Results and Discussion} \label{sec:results}
\input{chapters/results.tex}

\section{Conclusion} \label{sec:conclusion}
\input{chapters/conclusion.tex}

\section{Perspective} \label{sec:perspective}
\input{chapters/perspective.tex}

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\end{document}
