Best Linear Programming Books of 2026

Convex Optimization is a comprehensive introduction to solving convex optimization problems numerically. It is a great resource for students, researchers, and practitioners in fields such as engineering, computer science, mathematics, statistics, finance, and economics. The book is well-written, easy to follow, and contains many examples and exercises. The reviewer recommends it as one of the best optimization textbooks that have appeared in recent years. Overall, Convex Optimization is a must-read for anyone interested in optimization and computational mathematics.

The Fast Math Success Workbook Grade 7-8 is an excellent resource for children who require extra practice to excel in math. It covers a variety of math topics aligned with common core standards, from foundational concepts to more advanced topics like algebra, percent, geometry, inequalities, and Pythagorean theorem. The book provides sequential topics, allowing children to understand advanced topics only after grasping the basic concepts. The practice worksheets are carefully selected and provide solutions to all the problems. After completing this book, children will be able to recognize, understand, and solve math problems with confidence. This book is a great tool for teachers, homeschooling, and extra practice at home.

The Fast Math Success Workbook Grade 6-7 is a comprehensive math workbook that provides sequential topics from basic to advanced levels, helping children build a strong foundation in math. The book covers a variety of math topics aligned with common core standards and provides ample practice problems with solutions. This book is perfect for children who require extra practice to excel in math and for teachers who need a non-prep guide. The book is an excellent resource for homeschooling and can be used for math activities, extra practice, and homework. Overall, this book is a must-have for children who want to improve their math skills and build their confidence in school tests and exams.

This book, Numerical Optimization, is a comprehensive guide to optimization techniques used in decision science and engineering. It covers numerical methods for finite-dimensional optimization problems, progressing from simple to complex concepts and emphasizing methods for both unconstrained and constrained optimization. The book has received positive reviews from various sources, including Mathematical Methods of Operations Research, which recommends it for graduate-level courses in optimization. The reviewer also notes that the book is an excellent resource for those interested in completing their knowledge of optimization and finding new ideas.

Reinforcement Learning and Stochastic Optimization is a comprehensive guide that offers a single, unified framework for modeling any sequential decision problem. The book covers twelve types of uncertainty that can enter any model and pulls together the diverse set of methods for making decisions into four fundamental classes that span every method suggested in the academic literature or used in practice. The presentation is designed for readers with a course in probability and statistics, and an interest in modeling and applications. Overall, this book is an excellent resource for those looking to expand their knowledge of reinforcement learning and stochastic optimization.

The updated Second Edition of Generalized Linear Models provides a definitive unified treatment of methods for analyzing diverse types of data. The authors focus on examining the way a response variable depends on a combination of explanatory variables, treatment, and classification variables. The book is self-contained and requires only a knowledge of matrix theory and basic ideas of probability theory. With its worked examples, plentiful exercises, and topics of direct use to researchers in many disciplines, Generalized Linear Models serves as an ideal text, self-study guide, and reference. Overall, it is a well-written and rich book that is highly recommended for statisticians working in this field.

This book, "Excel 2023: The Most Updated Bible to Master Microsoft Excel from Zero To Pro in Less than 5 Minutes A Day" by Andrew J. Nash, is an excellent resource for both beginners and intermediate users of Excel. It covers everything from the basics like cells, indexing, rows and columns, to more advanced topics like macros, filters, VBA, and more. The author has condensed his experience as an Excel teacher into this book, making it easy to understand and follow. The book comes with free bonuses, including over 100 Excel templates and a video course. Overall, this book is a valuable tool for anyone looking to improve their Excel skills.

This book provides a comprehensive treatment of linear optimization, covering classical topics as well as the state of the art in theory and practice. The authors offer an easy-to-understand presentation of linear programming, network-flow programming, and discrete optimization. The book is highly pedagogical, providing geometric and intuitive explanations of various algebraic concepts. The authors' efforts in writing this book are impressive, making it an outstanding textbook that presents linear optimization in a modern and up-to-date light. This book is a must-read for teachers, researchers, and practitioners of mathematical programming.

The 4th edition of "Dynamic Programming and Optimal Control, Vol. I" by Bertsekas is a major revision of the leading two-volume dynamic programming textbook. The book covers both deterministic and stochastic control problems, in both discrete- and continuous-time, and it presents the Pontryagin minimum principle for deterministic control. The new material on approximate DP in Chapter 6 is particularly noteworthy, with an increased emphasis on one-step and multistep lookahead methods, parametric approximation architectures, neural networks, rollout, and Monte Carlo tree search. The book includes a substantial number of examples and exercises, and detailed solutions of many of which are posted on the internet. Overall, this book is an excellent resource for graduate students and researchers in the field of dynamic programming and optimal control.

The sixth edition of Stochastic Differential Equations an Introduction with Applications by Oksendal is a must-read for anyone interested in exploring the world of differential equations. The book is a comprehensive guide that introduces the reader to the concepts of stochastic calculus and its applications. It covers topics such as Brownian motion, stochastic integration, and stochastic differential equations, with examples and exercises that help the reader understand the material better. The book is written in a clear and concise language, making it accessible to both beginners and experts. Overall, this book is an excellent resource for anyone looking to dive deeper into the world of stochastic calculus.