Samia Challal – författare

Introduction to the Theory of Optimization in Euclidean Space is intended to provide students with a robust introduction to optimization in Euclidean space, demonstrating the theoretical aspects of the subject whilst also providing clear proofs and applications. Students are taken progressively through the development of the proofs, where they have the occasion to practice tools of differentiation (Chain rule, Taylor formula) for functions of several variables in abstract situations.Throughout this book, students will learn the necessity of referring to important results established in advanced Algebra and Analysis courses.Features Rigorous and practical, offering proofs and applications of theorems Suitable as a textbook for advanced undergraduate students on mathematics or economics courses, or as reference for graduate-level readers Introduces complex principles in a clear, illustrative fashion

Introduction to the Theory of Optimization in Euclidean Space is intended to provide students with a robust introduction to optimization in Euclidean space, demonstrating the theoretical aspects of the subject whilst also providing clear proofs and applications.

Students are taken progressively through the development of the proofs, where they have the occasion to practice tools of differentiation (Chain rule, Taylor formula) for functions of several variables in abstract situations.

Throughout this book, students will learn the necessity of referring to important results established in advanced Algebra and Analysis courses.

Features

Rigorous and practical, offering proofs and applications of theorems

Suitable as a textbook for advanced undergraduate students on mathematics or economics courses, or as reference for graduate-level readers

Introduces complex principles in a clear, illustrative fashion

Introduction to the Theory of Optimization in Euclidean Space is intended to provide students with a robust introduction to optimization in Euclidean space, demonstrating the theoretical aspects of the subject whilst also providing clear proofs and applications.

Students are taken progressively through the development of the proofs, where they have the occasion to practice tools of differentiation (Chain rule, Taylor formula) for functions of several variables in abstract situations.

Throughout this book, students will learn the necessity of referring to important results established in advanced Algebra and Analysis courses.

Features

Rigorous and practical, offering proofs and applications of theorems

Suitable as a textbook for advanced undergraduate students on mathematics or economics courses, or as reference for graduate-level readers

Introduces complex principles in a clear, illustrative fashion

Introduction to the Theory of Optimization in Euclidean Space is intended to provide students with a robust introduction to optimization in Euclidean space, demonstrating the theoretical aspects of the subject whilst also providing clear proofs and applications. Students are taken progressively through the development of the proofs, where they have the occasion to practice tools of differentiation (Chain rule, Taylor formula) for functions of several variables in abstract situations.Throughout this book, students will learn the necessity of referring to important results established in advanced Algebra and Analysis courses.Features Rigorous and practical, offering proofs and applications of theorems Suitable as a textbook for advanced undergraduate students on mathematics or economics courses, or as reference for graduate-level readers Introduces complex principles in a clear, illustrative fashion

This book is intended to equip the reader with concept knowledge on multilinear regression methods, and develop technical skills while solving problems with the R programming language. The goal is not to become an expert in both directions. It is rather an incentive to do research on advanced models in data analysis once confidence is gained with the practice of some methods.Basics in inferential statistics and basics in a programming language are necessary prerequisites to engage well with the content. We believe that using a computer is the only practical means of performing a linear or multilinear regression on a data set of even moderate size. Therefore, our purpose is to make these connections between theory, technology, and applications, as clear as possible.Features Focused summaries of the main statistical methods, followed by solved questions Integration of R as a calculator, and as a programming language in solving each question with ease and accuracy Use of simple coding that illustrates the connection to the theory Suggest progressively alternative codes Visualization and interpretation of the R outputs

This book is intended to equip the reader with concept knowledge on multilinear regression methods, and develop technical skills while solving problems with the R programming language. The goal is not to become an expert in both directions. It is rather an incentive to do research on advanced models in data analysis once confidence is gained with the practice of some methods.Basics in inferential statistics and basics in a programming language are necessary prerequisites to engage well with the content. We believe that using a computer is the only practical means of performing a linear or multilinear regression on a data set of even moderate size. Therefore, our purpose is to make these connections between theory, technology, and applications, as clear as possible.Features Focused summaries of the main statistical methods, followed by solved questions Integration of R as a calculator, and as a programming language in solving each question with ease and accuracy Use of simple coding that illustrates the connection to the theory Suggest progressively alternative codes Visualization and interpretation of the R outputs