Mathematical Software Icms 2024

This book constitutes the proceedings of the 8th International Conference on Mathematical Software, ICMS 2024, held in Durham, UK, during July 22–25, 2024. The 37 full papers presented were carefully reviewed and selected from 46 submissions. The papers are organized in subject areas as follows: plenary lectures; number theory and related areas; novel formalisations of mathematics in lean; software for the applications of group theory to combinatorics; classical algebraic geometry & modern computer algebra: innovative software design and its applications; advancing computer algebra with massively parallel methods; computer algebra applications in the life sciences; machine learning within computer algebra systems; numerical software for special functions; mathematical research data; symbolic-numeric methods in algebraic geometry; Polyhedral geometry and combinatorics; general session.

This book constitutes the proceedings of the 7th International Conference on Mathematical Software, ICMS 2020, held in Braunschweig, Germany, in July 2020. The 48 papers included in this volume were carefully reviewed and selected from 58 submissions. The program of the 2020 meeting consisted of 20 topical sessions, each of which providing an overview of the challenges, achievements and progress in a environment of mathematical software research, development and use.

This book highlights recent research advances in various domains related to software ecosystems such as library reuse, collaborative development, cloud computing, open science, sentiment analysis and machine learning. A key aspect of software ecosystems is that software products belong to ever more interdependent networks of co-evolving software components. The ever-increasing importance of social coding platforms has made software ecosystems indispensable to software practitioners, in commercial as well as open-source settings. The book starts with an introductory chapter that provides a historical account of the origins of software ecosystems. It provides the necessary context about the domain of software ecosystems by highlighting its different perspectives, definitions, and representations. It also exemplifies the variety of software ecosystems that have emerged during the previous decades. The remaining book is composed of five parts: Part I contains two chapters on software ecosystem representations, Part II two chapters that focus on complementary ways and techniques of analyzing software ecosystems. Next, Part III includes two chapters that focus on aspects related to the evolution within software ecosystems, while Part IV looks at workflow automation and infrastructure-as-code ecosystems. Finally, Part V focuses on ecosystems for software modeling and for data-intensive software. This book is intended for researchers and practitioners interested in data mining, tooling, and empirical analysis of software ecosystems. The reader will appreciate chapters that cover a wide spectrum of social and technical aspects of software ecosystems, each including an overview of the state of the art. Chapter 2 The Software Heritage Open Science Ecosystem is available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.

This book constitutes the proceedings of the 4th International Conference on Mathematical Software, ICMS 2014, held in Seoul, South Korea, in August 2014. The 108 papers included in this volume were carefully reviewed and selected from 150 submissions. The papers are organized in topical sections named: invited; exploration; group; coding; topology; algebraic; geometry; surfaces; reasoning; special; Groebner; triangular; parametric; interfaces and general.

The origins of computation group theory (CGT) date back to the late 19th and early 20th centuries. Since then, the field has flourished, particularly during the past 30 to 40 years, and today it remains a lively and active branch of mathematics. The Handbook of Computational Group Theory offers the first complete treatment of all the fundame

Zusammenfassung: Metric algebraic geometry combines concepts from algebraic geometry and differential geometry. Building on classical foundations, it offers practical tools for the 21st century. Many applied problems center around metric questions, such as optimization with respect to distances. After a short dive into 19th-century geometry of plane curves, we turn to problems expressed by polynomial equations over the real numbers. The solution sets are real algebraic varieties. Many of our metric problems arise in data science, optimization and statistics. These include minimizing Wasserstein distances in machine learning, maximum likelihood estimation, computing curvature, or minimizing the Euclidean distance to a variety. This book addresses a wide audience of researchers and students and can be used for a one-semester course at the graduate level. The key prerequisite is a solid foundation in undergraduate mathematics, especially in algebra and geometry. This is an open access book