Implement numerical algorithms in Java using NM Dev, an object-oriented and high-performance programming library for mathematics.You’ll see how it can help you easily create a solution for your complex engineering problem by quickly putting together classes. Numerical Methods Using Java covers a wide range of topics, including chapters on linear algebra, root finding, curve fitting, differentiation and integration, solving differential equations, random numbers and simulation, a whole suite of unconstrained and constrained optimization algorithms, statistics, regression and time series analysis. The mathematical concepts behind the algorithms are clearly explained, with plenty of code examples and illustrations to help even beginners get started. What You Will Learn Program in Java using a high-performance numerical library Learn the mathematics for a wide range of numerical computing algorithms Convert ideas and equations into code Put together algorithms and classes to build your own engineering solution Build solvers for industrial optimization problems Do data analysis using basic and advanced statistics Who This Book Is For Programmers, data scientists, and analysts with prior experience with programming in any language, especially Java.

Mak introduces Java programmers to numerical computing. This book contains clear, non-theoretical explanations of practical numerical algorithms, including safely summing numbers, finding roots of equations, interpolation and approximation, numerical integration and differentiation, and matrix operations, including solving sets of simultaneous equations.

Implement numerical algorithms in Java using the NM Dev, an object-oriented and high-performance programming library for mathematics.You'll see how it can help you easily create a solution for your complex engineering problem by quickly putting together classes. Numerical Methods Using Java covers a wide range of topics, including chapters on linear algebra, root finding, curve fitting, differentiation and integration, solving differential equations, random numbers and simulation, a whole suite of unconstrained and constrained optimization algorithms, statistics, regression and time series analysis. The mathematical concepts behind the algorithms are clearly explained, with plenty of code examples and illustrations to help even beginners get started. You will: Program in Java using a high-performance numerical library Learn the mathematics for a wide range of numerical computing algorithms Convert ideas and equations into code Put together algorithms and classes to build your own engineering solution Build solvers for industrial optimization problems Do data analysis using basic and advanced statistics.

"There are few books that show how to build programs of any kind. One common theme is compiler building, and there are shelves full of them. There are few others. It's an area, or a void, that needs filling. this book does a great job of showing how to build numerical analysis programs." -David N. Smith, IBM T J Watson Research Center Numerical methods naturally lend themselves to an object-oriented approach. Mathematics builds high- level ideas on top of previously described, simpler ones. Once a property is demonstrated for a given concept, it can be applied to any new concept sharing the same premise as the original one, similar to the ideas of reuse and inheritance in object-oriented (OO) methodology. Few books on numerical methods teach developers much about designing and building good code. Good computing routines are problem-specific. Insight and understanding are what is needed, rather than just recipes and black box routines. Developers need the ability to construct new programs for different applications. Object-Oriented Implementation of Numerical Methods reveals a complete OO design methodology in a clear and systematic way. Each method is presented in a consistent format, beginning with a short explanation and following with a description of the general OO architecture for the algorithm. Next, the code implementations are discussed and presented along with real-world examples that the author, an experienced software engineer, has used in a variety of commercial applications. Features: Reveals the design methodology behind the code, including design patterns where appropriate, rather than just presenting canned solutions. Implements all methods side by side in both Java and Smalltalk. This contrast can significantly enhance your understanding of the nature of OO programming languages. Provides a step-by-step pathway to new object-oriented techniques for programmers familiar with using procedural languages such as C or Fortran for numerical methods. Includes a chapter on data mining, a key application of numerical methods.

An elementary first course for students in mathematics and engineering Practical in approach: examples of code are provided for students to debug, and tasks – with full solutions – are provided at the end of each chapter Includes a glossary of useful terms, with each term supported by an example of the syntaxes commonly encountered

Numerical Methods and Programming has been written for engineering students of all streams, and can also be used profitably by all degree students. Theories have been discussed comprehensively, with numerous solved problems to help students understand subsequent techniques. The C programs in the book will be of immense help to the students in solving complex problems. The authors’ long experiences of teaching various grades of students have played an instrumental role towards this end. Key Features • Brief but sufficient discussion of theory • Lucid presentation of theoretical concepts • Simple and easy-to-understand language • Solutions for a large number of technical problems • Examination-oriented approach • Several multiple choice questions with answers • Latest and previous years’ university question papers

Computationally-intensive tools play an increasingly important role in financial decisions. Many financial problems-ranging from asset allocation to risk management and from option pricing to model calibration-can be efficiently handled using modern computational techniques. Numerical Methods and Optimization in Finance presents such computational techniques, with an emphasis on simulation and optimization, particularly so-called heuristics. This book treats quantitative analysis as an essentially computational discipline in which applications are put into software form and tested empirically. This revised edition includes two new chapters, a self-contained tutorial on implementing and using heuristics, and an explanation of software used for testing portfolio-selection models. Postgraduate students, researchers in programs on quantitative and computational finance, and practitioners in banks and other financial companies can benefit from this second edition of Numerical Methods and Optimization in Finance. Introduces numerical methods to readers with economics backgrounds Emphasizes core simulation and optimization problems Includes MATLAB and R code for all applications, with sample code in the text and freely available for download