Optimization for Data Analysis

Optimization techniques are essential for data science, and this text covers fundamental methods such as gradient and accelerated gradient methods, stochastic gradient method, coordinate descent approach, and algorithms for constrained optimization problems. It also discusses nonsmooth functions, analysis of nonsmooth functions, and optimization duality.

Format: Hardback
Length: 238 pages
Publication date: 21 April 2022
Publisher: Cambridge University Press

Data science is at its core, and optimization techniques play a pivotal role in this field. These techniques encompass data analysis and machine learning, providing a solid foundation for students, researchers, and practitioners alike. By understanding the fundamentals of optimization algorithms and their properties, individuals can delve into the world of data science with greater confidence.

This text aims to provide a comprehensive yet concise introduction to optimization algorithms in the context of data science. It begins by highlighting the fact that many standard problems encountered in data science can be formulated as optimization problems. This introductory chapter sets the stage for the subsequent chapters, which delve into various optimization methods.

Gradient and accelerated gradient methods are discussed in detail, focusing on their application to unconstrained optimization of smooth functions, particularly convex ones. These methods are fundamental in solving a wide range of optimization problems encountered in data science.

The stochastic gradient method is introduced as a powerful workhorse algorithm in machine learning. It enables the optimization of complex functions with high-dimensional data, making it invaluable in tasks such as deep learning and natural language processing.

The coordinate descent approach is explored, which is a widely used method for unconstrained optimization. It involves iteratively updating the parameters of a function to minimize its value. This approach is particularly useful in solving large-scale optimization problems.

Several key algorithms for constrained optimization problems are discussed, including linear programming, nonlinear programming, and mixed-integer programming. These algorithms are designed to handle complex optimization scenarios where constraints must be considered.

Nonsmooth functions, which arise frequently in data science, are addressed. Algorithms for minimizing nonsmooth functions, such as smooth optimization, are discussed, along with the foundations of the analysis of nonsmooth functions and optimization duality.

The back-propagation approach, which is relevant to neural networks, is introduced. It involves updating the weights of neural networks in a backward manner, based on the error between the predicted and actual outputs. This approach is widely used in training deep learning models.

In conclusion, optimization techniques are essential in data science, and this text provides a comprehensive introduction to the fundamentals of optimization algorithms. By understanding these methods and their properties, individuals can gain a deeper understanding of data science and apply them to solve complex problems in this field.

Weight: 454g
Dimension: 156 x 236 x 18 (mm)
ISBN-13: 9781316518984