Best Linear Programming Books

Here you will get Best Linear Programming Books For you.This is an up-to-date list of recommended books.

1. 1st Grade Common Core Math: Daily Practice Workbook | 1000+ Practice Questions and Video Explanations | Argo Brothers (Common Core Math by ArgoPrep)

Author: by Argo Brothers
Published at: Argo Brothers Inc (January 5, 2019)
ISBN: 978-1946755513

View on Amazon

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 PracticeWeekly AssessmentsState Aligned Common Core CurriculumEnd of Year Assessment This book has following topics covered:Week 1 – Adding and subtracting within 20Week 2- Word problems that involve three whole numbersWeek 3 – Properties of operationsWeek 4 – Subtraction as an unknown-addend problemWeek 5 – Add and subtract numbers within 20Week 6 – Secrets of how to add and subtractWeek 7 – The equal signWeek 8 – Add or subtract three whole numbersWeek 9 – Count to the number 120Week 10 – Learning about the ones and tens place valueWeek 11 – Compare two digit numbersWeek 12 – Add and subtract within 100Week 13 – Finding 10 more or 10 less than a number mentallyWeek 14 – Subtract multiples of 10 using models and drawingsWeek 15 – Order three objects by lengthWeek 16 – Adding and subtracting using equivalent numbersWeek 17 – Learn and write about timeWeek 18 – Representing and interpreting dataWeek 19 – Different shapes and their attributesWeek 20 – Two-dimensional shapes, three-dimensional shapes and how to partition circles and rectangles into two or four equal partsEnd of Year AssessmentEach question is labeled with the specific common core standard so both parents and teachers can use this workbook for their student(s).

2. Convex Optimization

Author: by Stephen Boyd
Published at: Cambridge University Press; 1st edition (March 1, 2004)
ISBN: 978-0521833783

View on Amazon

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)

Author: by Jorge Nocedal
Published at: Springer; 2nd edition (July 27, 2006)
ISBN: 978-0387303031

View on Amazon

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. Hierarchical Linear Models: Applications and Data Analysis Methods (Advanced Quantitative Techniques in the Social Sciences)

Author: by Stephen W. Raudenbush
Published at: SAGE Publications, Inc; 2nd edition (December 19, 2001)
ISBN: 978-0761919049

View on Amazon

Popular in the First Edition for its rich, illustrative examples and lucid explanations of the theory and use of hierarchical linear models (HLM), the book has been reorganized into four parts with four completely new chapters. The first two parts, Part I on “The Logic of Hierarchical Linear Modeling” and Part II on “Basic Applications” closely parallel the first nine chapters of the previous edition with significant expansions and technical clarifications, such as: * An intuitive introductory summary of the basic procedures for estimation and inference used with HLM models that only requires a minimal level of mathematical sophistication in Chapter 3* New section on multivariate growth models in Chapter 6 * A discussion of research synthesis or meta-analysis applications in Chapter 7* Data analytic advice on centering of level-1 predictors and new material on plausible value intervals and robust standard estimators

5. Dynamic Programming and Optimal Control, Vol. I, 4th Edition

Author: by Dimitri Bertsekas
Published at: Athena Scientific; 4th edition (February 6, 2017)
ISBN: 978-1886529434

View on Amazon

This 4th edition is a major revision of Vol. I of the leading two-volume dynamic programming textbook by Bertsekas, and contains a substantial amount of new material, particularly on approximate DP in Chapter 6. This chapter was thoroughly reorganized and rewritten, to bring it in line, both with the contents of Vol.

II, whose latest edition appeared in 2012, and with recent developments, which have propelled approximate DP to the forefront of attention. Some of the highlights of the revision of Chapter 6 are an increased emphasis on one-step and multistep lookahead methods, parametric approximation architectures, neural networks, rollout, and Monte Carlo tree search.

Among other applications, these methods have been instrumental in the recent spectacular success of computer Go programs. The material on approximate DP also provides an introduction and some perspective for the more analytically oriented treatment of Vol.II. The book includes a substantial number of examples, and exercises, detailed solutions of many of which are posted on the internet.

6. Graph Paper Math Squared Notebook: Graph Paper For Kids Large 1/2 Inch Squares (Graph Paper Notebook 1/2 Inch Squares)

Author: by Peter Graph Paper
Published at: Independently published (March 31, 2019)
ISBN: 978-1092239547

View on Amazon

Graph Paper Math Squared Notebook 1/2 Inch Squares, Soft Cover, Non-Perforated, Non-SpiralSpecification:124 pages – 8.5 x 11 (21.59cm x 27. 94cm)Lightly lined pages, Quad ruled 2×2 2 squares per inch. High quality paper Minimal ink bleed-through. Soft matte cover bound Non-spiralGood quality white paper perfect for drawing, math, graphing and science design

7. Practical Discrete Mathematics: Discover math principles that fuel algorithms for computer science and machine learning with Python

Author: by Ryan T. White
Published at: Packt Publishing (February 22, 2021)
ISBN: 978-1838983147

View on Amazon

A practical guide simplifying discrete math for curious minds and demonstrating its application in solving problems related to software development, computer algorithms, and data scienceKey FeaturesApply the math of countable objects to practical problems in computer scienceExplore modern Python libraries such as scikit-learn, NumPy, and SciPy for performing mathematicsLearn complex statistical and mathematical concepts with the help of hands-on examples and expert guidanceBook DescriptionDiscrete mathematics deals with studying countable, distinct elements, and its principles are widely used in building algorithms for computer science and data science.

The knowledge of discrete math concepts will help you understand the algorithms, binary, and general mathematics that sit at the core of data-driven tasks. Practical Discrete Mathematics is a comprehensive introduction for those who are new to the mathematics of countable objects.

This book will help you get up to speed with using discrete math principles to take your computer science skills to a more advanced level. As you learn the language of discrete mathematics, you’ll also cover methods crucial to studying and describing computer science and machine learning objects and algorithms.

8. Linear Models with R (Chapman & Hall/CRC Texts in Statistical Science)

Author: by Julian J. Faraway
Published at: Chapman and Hall/CRC; 2nd edition (July 1, 2014)
ISBN: 978-1439887332

View on Amazon

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.

9. Model Building in Mathematical Programming

Author: by H. Paul Williams
Published at: Wiley; 5th edition (March 4, 2013)
ISBN: 978-1118443330

View on Amazon

The 5th edition of Model Building in Mathematical Programming discusses the general principles of model building in mathematical programming and demonstrates how they can be applied by using several simplified but practical problems from widely different contexts. Suggested formulations and solutions are given together with some computational experience to give the reader a feel for the computational difficulty of solving that particular type of model.

Furthermore, this book illustrates the scope and limitations of mathematical programming, and shows how it can be applied to real situations. By emphasizing the importance of the building and interpreting of models rather than the solution process, the author attempts to fill a gap left by the many works which concentrate on the algorithmic side of the subject.In this article, H.P.

Williams explains his original motivation and objectives in writing the book, how it has been modified and updated over the years, what is new in this edition and why it has maintained its relevance and popularity over the years:

10. Linear Mixed Models: A Practical Guide Using Statistical Software, Second Edition

Author: by Brady T. West
Published at: Chapman and Hall/CRC; 2nd edition (July 17, 2014)
ISBN: 978-1466560994

View on Amazon

Highly recommended by JASA, Technometrics, and other journals, the first edition of this bestseller showed how to easily perform complex linear mixed model (LMM) analyses via a variety of software programs. Linear Mixed Models: A Practical Guide Using Statistical Software, Second Edition continues to lead readers step by step through the process of fitting LMMs. This second edition covers additional topics on the application of LMMs that are valuable for data analysts in all fields.

It also updates the case studies using the latest versions of the software procedures and provides up-to-date information on the options and features of the software procedures available for fitting LMMs in SAS, SPSS, Stata, R/S-plus, and HLM. New to the Second Edition A new chapter on models with crossed random effects that uses a case study to illustrate software procedures capable of fitting these models Power analysis methods for longitudinal and clustered study designs, including software options for power analyses and suggested approaches to writing simulations Use of the lmer function in the lme4 R package New sections on fitting LMMs to complex sample survey data and Bayesian approaches to making inferences based on LMMs Updated graphical procedures in the software packages Substantially revised index to enable more efficient reading and easier location of material on selected topics or software options More practical recommendations on using the software for analysis A new R package (WWGbook) that contains all of the data sets used in the examples Ideal for anyone who uses software for statistical modeling, this book eliminates the need to read multiple software-specific texts by covering the most popular software programs for fitting LMMs in one handy guide.

11. Nonlinear Systems

Author: by Hassan Khalil
Published at: Pearson; 3rd edition (December 18, 2001)
ISBN: 978-0130673893

View on Amazon

This book is written is such a way that the level of mathematical sophistication builds up from chapter to chapter. It has been reorganized into four parts: basic analysis, analysis of feedback systems, advanced analysis, and nonlinear feedback control. KEY TOPICS: Updated content includes subjects which have proven useful in nonlinear control design in recent yearsnew in the 3rd edition are: expanded treatment of passivity and passivity-based control; integral control, high-gain feedback, recursive methods, optimal stabilizing control, control Lyapunov functions, and observers.

MARKET: For use as a self-study or reference guide by engineers and applied mathematicians.

12. Topics in Optimal Transportation (Graduate Studies in Mathematics, Vol. 58)

Author: by Cedric Villani
Published at: American Mathematical Society; UK ed. edition (March 1, 2003)
ISBN: 978-0821833124

View on Amazon

This is the first comprehensive introduction to the theory of mass transportation with its many-and sometimes unexpected-applications. In a novel approach to the subject, the book both surveys the topic and includes a chapter of problems, making it a particularly useful graduate textbook.

In 1781, Gaspard Monge defined the problem of “optimal transportation” (or the transferring of mass with the least possible amount of work), with applications to engineering in mind. In 1942, Leonid Kantorovich applied the newborn machinery of linear programming to Monge’s problem, with applications to economics in mind.

In 1987, Yann Brenier used optimal transportation to prove a new projection theorem on the set of measure preserving maps, with applications to fluid mechanics in mind. Each of these contributions marked the beginning of a whole mathematical theory, with many unexpected ramifications.

Nowadays, the Monge-Kantorovich problem is used and studied by researchers from extremely diverse horizons, including probability theory, functional analysis, isoperimetry, partial differential equations, and even meteorology. Originating from a graduate course, the present volume is intended for graduate students and researchers, covering both theory and applications.

13. Adaptive Nonlinear System Identification: The Volterra and Wiener Model Approaches (Signals and Communication Technology)

Author: by Tokunbo Ogunfunmi
Published at: Springer; 2007th edition (September 12, 2007)
ISBN: 978-0387263281

View on Amazon

Focuses on System Identification applications of the adaptive methods presented. But which can also be applied to other applications of adaptive nonlinear processes. Covers recent research results in the area of adaptive nonlinear system identification from the authors and other researchers in the field.

14. Behavioral Decision Theory (A New Approach)

Author: by E. C. Poulton

Published at: Cambridge University Press; 1st edition (September 1, 1994)
ISBN: 978-0521443685

View on Amazon

This book discusses the well-known fallacies of behavioral decision theory. It shows that while an investigator is studying a fallacy, he or she may introduce without realizing it, one of the simple biases that are found in quantifying judgments. The work covers such fallacies as the apparent overconfidence that people show when they judge the probability of correctness of their answers to two-choice general knowledge questions using a one-sided rating scale; the apparent overconfidence in setting uncertainty bounds on unknown quantities when using the fractile method; the interactions between hindsight and memory; the belief that small samples are as reliable and as representative as are large samples; the conjunction fallacy for Linda and Bill; the causal conjunction fallacy; the regression fallacy in prediction; the neglect of the base rate in the Cab problem, in predicting professions, and in the Medical Diagnosis problem; the availability and simulation fallacies; the anchoring and adjustment biases; Prospect theory; and bias by frames.