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The 10 Best Linear Programming Books list have been recommended not only by normal readers but also by experts.
You’ll also find that these are top-ranking books on the US Amazon Best Sellers book list for the Linear Programming category of books.
If any of the titles interest you, I’d recommend checking them out by clicking the “Check Price” button. It’ll take you to the authorized retailer website, where you’ll be able to see reviews and buy it.
Let’s take a look at the list of 10 Best Linear Programming Books.
10 Best Linear Programming Books
Now, let’s dive right into the list of 10 Best Linear Programming Books, where we’ll provide a quick outline for each book.
1. 1st Grade Common Core Math: Daily Practice Workbook | 1000+ Practice Questions and Video Explanations | Argo Brothers by Argo Brothers Review Summary
1st Grade Common Core Math: Daily Practice Workbook | 1000+ Practice Questions and Video Explanations | Argo Brothers
ArgoPrep is a recipient of the prestigious Mom’s Choice Award. ArgoPrep also received the 2019 Seal of Approval from Homeschool.com for our award-winning workbooks. ArgoPrep was awarded the 2019 National Parenting Products Award and a Gold Medal Parent’s Choice Award. ## This book is your comprehensive workbook for 1st Grade Common Core Math. By practicing and mastering this entire workbook, your child will become very familiar and comfortable with the state math exam and common core standards. This 1st Grade Common Core Math Daily Practice Workbook includes: * 20 Weeks of Daily Math Practice * Weekly Assessments * State Aligned Common Core Curriculum * End of Year Assessment #### This book has following topics covered : Week 1 – Adding and subtracting within 20 Week 2- Word problems that involve three whole numbers Week 3 – Properties of operations Week 4 – Subtraction as an unknown-addend problem Week 5 – Add and subtract numbers within 20 Week 6 – Secrets of how to add and subtract Week 7 – The equal sign Week 8 – Add or subtract three whole numbers Week 9 – Count to the number 120 Week 10 – Learning about the ones and tens place value Week 11 – Compare two digit numbers Week 12 – Add and subtract within 100 Week 13 – Finding 10 more or 10 less than a number mentally Week 14 – S ubtract multiples of 10 using models and drawings Week 15 – Order three objects by length Week 16 – Adding and subtracting using equivalent numbers Week 17 – Learn and write about time Week 18 – Representing and interpreting data Week 19 – Different shapes and their attributes Week 20 – Two-dimensional shapes, three-dimensional shapes and how to partition circles and rectangles into two or four equal parts End of Year Assessment Each question is labeled with the specific common core standard so both parents and teachers can use this workbook for their student(s). This workbook takes the Common Core State Standards and divides them up among 20 weeks. By working on these problems on a daily basis, students will be able to (1) find any deficiencies in their understanding and/or practice of math and (2) have small successes each day that will build competence and confidence in their abilities.
2. Convex Optimization by Stephen Boyd Review Summary
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Convex Optimization
Convex optimization problems arise frequently in many different fields. A comprehensive introduction to the subject, this book shows in detail how such problems can be solved numerically with great efficiency. The focus is on recognizing convex optimization problems and then finding the most appropriate technique for solving them. The text contains many worked examples and homework exercises and will appeal to students, researchers and practitioners in fields such as engineering, computer science, mathematics, statistics, finance, and economics.
3. Numerical Optimization (Springer Series in Operations Research and Financial Engineering) by Jorge Nocedal Review Summary
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Numerical Optimization (Springer Series in Operations Research and Financial Engineering)
Optimization is an important tool used in decision science and for the analysis of physical systems used in engineering. One can trace its roots to the Calculus of Variations and the work of Euler and Lagrange. This natural and reasonable approach to mathematical programming covers numerical methods for finite-dimensional optimization problems. It begins with very simple ideas progressing through more complicated concepts, concentrating on methods for both unconstrained and constrained optimization.
4. Linear Models with R (Chapman & Hall/CRC Texts in Statistical Science) by Julian J. Faraway Review Summary
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Linear Models with R (Chapman & Hall/CRC Texts in Statistical Science)
A Hands-On Way to Learning Data Analysis Part of the core of statistics, linear models are used to make predictions and explain the relationship between the response and the predictors. Understanding linear models is crucial to a broader competence in the practice of statistics. Linear Models with R, Second Edition explains how to use linear models in physical science, engineering, social science, and business applications. The book incorporates several improvements that reflect how the world of R has greatly expanded since the publication of the first edition. New to the Second Edition * Reorganized material on interpreting linear models, which distinguishes the main applications of prediction and explanation and introduces elementary notions of causality * Additional topics, including QR decomposition, splines, additive models, Lasso, multiple imputation, and false discovery rates * Extensive use of the ggplot2 graphics package in addition to base graphics Like its widely praised, best-selling predecessor, this edition combines statistics and R to seamlessly give a coherent exposition of the practice of linear modeling. The text offers up-to-date insight on essential data analysis topics, from estimation, inference, and prediction to missing data, factorial models, and block designs. Numerous examples illustrate how to apply the different methods using R.
5. The Golden Ticket: P, NP, and the Search for the Impossible by Lance Fortnow Review Summary
The Golden Ticket: P, NP, and the Search for the Impossible
The P-NP problem is the most important open problem in computer science, if not all of mathematics. Simply stated, it asks whether every problem whose solution can be quickly checked by computer can also be quickly solved by computer. The Golden Ticket provides a nontechnical introduction to P-NP, its rich history, and its algorithmic implications for everything we do with computers and beyond. Lance Fortnow traces the history and development of P-NP, giving examples from a variety of disciplines, including economics, physics, and biology. He explores problems that capture the full difficulty of the P-NP dilemma, from discovering the shortest route through all the rides at Disney World to finding large groups of friends on Facebook. The Golden Ticket explores what we truly can and cannot achieve computationally, describing the benefits and unexpected challenges of this compelling problem.
6. Machine Learning: The Most Complete Guide for Beginners to Mastering Deep Learning, Artificial Intelligence and Data Science with Python. This Book Includes: Python Machine Learning and Data Science. by Andrew Park Review Summary
Machine Learning: The Most Complete Guide for Beginners to Mastering Deep Learning, Artificial Intelligence and Data Science with Python. This Book ... and Data Science. (Data Science Mastery)
## Master The World Of Machine Learning And Data Science With This Comprehensive 2-in-1 bundle ### If you want to learn more about Machine Learning and Data Science or how to master them with Python quickly and easily, then keep reading. Data Science and Machine Learning are one of the biggest buzzwords in the business world nowadays. Many businesses know the importance of collecting information, but as they can collect so much data in a short period, the real question is: “what is the next step?” Data Science includes all the different steps that you take with the data: collecting and cleaning them, analyzing them, applying Machine Learning algorithms and models, and then presenting your findings from the analysis with some good Data Visualizations. Machines and automation represent a huge part of our daily life. They are becoming part of our experience, and existence. Artificial Intelligence is currently one of the most thriving fields any programmer would wish to delve into, and for a good reason: this is the future! Simply put, Machine Learning is about teaching machines to think and make decisions as we would. The difference between the way machines learn and the way we do is that while for the most part we learn from experiences, machines learn from data. ### In book one, PYTHON MACHINE LEARNING, you will learn: * What is Machine Learning and how it is applied in real-world situations * Understanding the differences between Machine Learning, Deep Learning, and Artificial Intelligence * Machine learning training models, Regression techniques and Linear Regression in Python * How to use Lists and Modules in Python * The 12 essential libraries for Machine Learning in Python * Artificial Neural Networks * And Much More! ### In book two, PYTHON DATA SCIENCE, you will learn: * What Data Science is all about and why so many companies are using it to give them a competitive edge. * Why Python and how to use it to implement Data Science * The main Data Structures & Object-Oriented Programming, Functions and Modules in Python with practical codes and exercises * The 7 most important algorithms and models in Data Science * Data Aggregation, Group Operations, Databases and Data in the Cloud * 9 important Data Mining techniques in Data Science * And So Much More! Where most books only focus on how collecting and cleaning the data, this book goes further, providing guidance on how to perform a proper analysis in order to extract precious information that may be vital for a business. Don’t miss the opportunity to master the key points of Machine Learning technology and understand how researchers are breaking the boundaries of Data Science to mimic human intelligence in machines. Even if some Machine Learning concepts and algorithms can appear complex to most computer programming beginners, this book takes the time to explain them in a simple and concise way. Understanding Machine Learning and Data Science is easier than it looks. You just need the right guidance. And this bundle provides all the knowledge you need in a simple and practical way. Regardless of your previous experience, you will learn the techniques to manipulate and process datasets, the principles of Python programming, and its most important real-world applications. ### Would You Like To Know More? ## Scroll Up and Click the BUY NOW Button to Get Your Copy!
7. Graph Paper Math Squared Notebook: Graph Paper For Kids Large 1/2 Inch Squares (Graph Paper Notebook 1/2 Inch Squares) by Peter Graph Paper Review Summary
8. Dynamic Programming (Dover Books on Computer Science) by Richard Bellman Review Summary
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Dynamic Programming (Dover Books on Computer Science)
An introduction to the mathematical theory of multistage decision processes, this text takes a “functional equation” approach to the discovery of optimum policies. Written by a leading developer of such policies, it presents a series of methods, uniqueness and existence theorems, and examples for solving the relevant equations. The text examines existence and uniqueness theorems, the optimal inventory equation, bottleneck problems in multistage production processes, a new formalism in the calculus of variation, strategies behind multistage games, and Markovian decision processes. Each chapter concludes with a problem set that Eric V. Denardo of Yale University, in his informative new introduction, calls “a rich lode of applications and research topics.” 1957 edition. 37 figures.
9. Optimal Control of Mechanical Oscillations (Foundations of Engineering Mechanics) by Agnessa Kovaleva Review Summary
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Optimal Control of Mechanical Oscillations (Foundations of Engineering Mechanics)
This book explores two important aspects of the optimal control of oscillatory systems: the initiation of optimal oscillatory regimes and control possibilities for random disturbances. The main content of the book is based upon assertions of the optimal control theory and the disturbance theory. All theoretical propositions are illustrated by examples with exact mechanical context. An appendix covers the necessary mathematical prerequisites.
10. Finite-Dimensional Variational Inequalities and Complementarity Problems (Springer Series in Operations Research and Financial Engineering) by Francisco Facchinei Review Summary
