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Applied Numerical Linear Algebra
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Applied Numerical Linear Algebra
This comprehensive textbook covers the solution of linear systems, least squares problems, eigenvalue problems, and the singular value decomposition.
Applied Numerical Linear Algebra
Item #: 33345449

Applied Numerical Linear Algebra

86% of respondents would recommend this to a friend

Item #: 33345449

BOB 1185

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This comprehensive textbook covers the solution of linear systems, least squares problems, eigenvalue problems, and the singular value decomposition.
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What Stands Out

Comprehensive Coverage
Offers extensive exploration of numerical linear algebra concepts, making it suitable for both students and professionals seeking to deepen their understanding and application of these essential mathematical techniques.
Practical Applications
Emphasizes real-world applications in engineering and science, helping users connect theoretical concepts with practical scenarios, thereby enhancing learning and retention of key principles.
User-Friendly Design
Incorporates clear explanations and illustrative examples, ensuring accessibility for learners at different levels, and thereby improving comprehension and engagement with complex numerical methods.

Product Details

Discover the Illustrated Edition of Applied Numerical Linear Algebra at Ubuy Bolivia. Perfect for students and professionals. Order now and enhance your mathematical skills!
Publisher SIAM
Publication date August 1, 1997
Edition 1st
Language English
Print length 184 pages
ISBN-10 0898713897
ISBN-13 978-0898713893
Item Weight 1.89 pounds (860 grams)
Dimensions 7 x 1 x 10.25 inches (17.8 x 2.5 x 26 cm)
Country of OriginThis item will be imported from US
Date First AvailableMay 17, 2021
What is in the boxApplied Numerical Linear Algebra... For more details, please check description/product details

Who Should Buy?

Suitable For
  • Graduate Students

    Ideal for graduate students in mathematics or engineering who need a solid understanding of numerical linear algebra techniques.

  • Professionals in Data Science

    Data scientists who require efficient algorithms for large data sets will greatly benefit from the concepts in this book.

  • Researchers and Academics

    Academics requiring comprehensive methodologies for research in numerical analysis can enhance their understanding through these practical applications.

Not Suitable For
  • Beginner Users

    Those new to linear algebra may find the content too advanced without prior foundational knowledge and training.

Product Description

Applied Numerical Linear Algebra

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Customer Questions & Answers

  • Question: What is 'Applied Numerical Linear Algebra' about?

    Answer: Applied Numerical Linear Algebra focuses on the techniques and methods for solving linear algebra problems using numerical approaches. It combines theoretical aspects with practical applications, allowing students and professionals to tackle real-world challenges in engineering, computer science, and data analysis. This field is essential for efficiently handling large datasets, optimizing systems, and simulations, making it invaluable in scenarios like machine learning or scientific computing.
  • Question: Who should consider studying Applied Numerical Linear Algebra?

    Answer: This subject is ideal for students in mathematics, engineering, computer science, and data science. Professionals in fields that require optimization, modeling, or simulations, such as financial analysts and software developers, will also benefit greatly. Understanding the principles can help improve problem-solving skills and enhance one’s ability to manipulate complex data, paving the way for advancements in various technical roles.
  • Question: Is prior knowledge of linear algebra required for this course?

    Answer: Yes, a solid foundation in linear algebra is typically recommended before diving into Applied Numerical Linear Algebra. Understanding concepts such as matrices, vectors, eigenvalues, and eigenvectors is crucial. This prior knowledge ensures that you can better grasp the numerical techniques and their applications, allowing you to effectively apply these concepts in practical scenarios like solving differential equations or optimizing algorithms.
  • Question: What are some practical applications of Applied Numerical Linear Algebra?

    Answer: Applied Numerical Linear Algebra has numerous applications across various domains. In engineering, it is used for structural analysis and simulations. In data science, it aids in processing and analyzing large data sets through methods like Principal Component Analysis (PCA). Additionally, it plays a key role in machine learning algorithms, where performance enhancement through efficient computations is vital for handling vast amounts of data.
  • Question: How does this subject relate to data science?

    Answer: This subject is pivotal in data science, as it provides the tools to manipulate and analyze large datasets. Techniques in numerical linear algebra are often employed in algorithms for regression, classification, and dimensionality reduction. Understanding these concepts allows data scientists to improve the performance of models and accurately interpret results, enabling them to derive meaningful insights from complex data.
  • Question: What resources are recommended for studying this subject?

    Answer: Key resources include textbooks focusing on numerical linear algebra, online courses, and research papers. Popular textbooks often used are 'Numerical Linear Algebra' by Trefethen and Bau, which provides thorough explanations and applications. Additionally, online platforms such as Coursera or edX may offer relevant courses that supplement learning through interactive content and real-world problem-solving exercises.
  • Question: Can I find software tools related to Applied Numerical Linear Algebra?

    Answer: Yes, there are several software tools available that facilitate the application of numerical linear algebra concepts. MATLAB, NumPy in Python, and R are popular choices for implementing algorithms and performing matrix operations. These tools provide libraries that streamline calculations, helping users apply the mathematical concepts learned in the study to tackle practical problems efficiently in various industries.
  • Question: What skills can I develop through studying this course?

    Answer: Studying Applied Numerical Linear Algebra helps develop problem-solving skills, computational thinking, and proficiency in mathematical modeling. Additionally, it enhances your ability to work with algorithms and software tools, making you more adept in analytical thinking and data interpretation. These skills are essential for pursuing careers in technical fields, research, and academia, allowing for greater effectiveness in a data-driven environment.
  • Question: How can this knowledge improve my career prospects?

    Answer: Knowledge of Applied Numerical Linear Algebra can significantly enhance your career prospects in tech-heavy industries. Expertise in this area is sought after in roles related to data analysis, engineering, quantitative finance, and artificial intelligence. Employers value candidates who can tackle complex problems and optimize processes, making this skill set a desirable addition to your resume and positioning you for advancement.
  • Question: Where can I buy Applied Numerical Linear Algebra in Bolivia?

    Answer: You can purchase Applied Numerical Linear Algebra through Ubuy, which provides a seamless shopping experience for customers in Bolivia. Ubuy offers a variety of editions and formats, making it easy for you to find the right resource for your studies. Their platform typically features various payment options and shipping methods tailored to meet your needs, ensuring you get your hands on this essential book with convenience.

Linear Editorial Review

Applied Numerical Linear Algebra is an insightful reference for those diving deep into numerical methods for solving linear equations. The author, James Demmel, covers essential topics including eigenvalue problems and floating point arithmetic errors, making it invaluable for both students and professionals in the field. Many reviews highlight the book's thoroughness and detailed explanations, though some users caution that it may be daunting for beginners without a strong mathematical foundation. Despite the complexity, the comprehensive nature of this text aids in understanding various algorithms and provides an excellent resource for identifying potential pitfalls in numerical computations.

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Pros

  • Comprehensive coverage of numerical linear algebra topics
  • Insightful explanations of common numerical pitfalls
  • Great resource for graduate-level studies
  • Well-organized structure and clear presentations
  • Helpful for applying methods to real-world problems

Cons

  • Binding quality may be a concern for some readers

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Features & Benefits

  • Designed for first-year graduate students in engineering and science.
  • Covers key topics like linear systems, least squares, eigenvalue problems, and singular value decomposition.
  • Written by an expert who developed widely used LAPACK and ScaLAPACK libraries.
  • Offers state-of-the-art techniques and algorithm recommendations for practical situations.
  • Includes numerous numerical examples and exercises using MATLAB, freely available online.
  • Discusses algorithm design tailored for modern computer architectures.

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