May 1, 2024
Updated May 10, 2025
23 minute read
Numerical integration is a cornerstone of computational science, providing essential tools for approximating the value of definite integrals. In essence, it's a collection of algorithms designed to find the numerical solution to an integral, which geometrically represents the area under a curve. This technique becomes indispensable when an analytical solution—finding an exact antiderivative—is difficult or impossible to achieve. The field is also commonly referred to as numerical quadrature, particularly for one-dimensional integrals.
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Reading list
We've selected nine books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Numerical Integration.
This classic book provides a comprehensive collection of numerical recipes for a wide range of scientific computing problems. It includes a chapter on numerical integration, which covers a variety of methods, including quadrature formulas, Gaussian quadrature, and Monte Carlo integration.
Provides a comprehensive overview of numerical integration and its applications. It covers a wide range of methods, from basic quadrature formulas to advanced Monte Carlo techniques. It also includes applications to a variety of problems in science and engineering.
Provides a comprehensive overview of numerical integration of differential equations. It covers a wide range of topics, from basic concepts to advanced techniques. It is suitable for both researchers and practitioners.
This classic textbook provides a broad overview of numerical analysis, including numerical integration. It covers a wide range of topics, from basic concepts to advanced techniques. It is suitable for both undergraduate and graduate students.
Provides a comprehensive overview of Monte Carlo methods in financial engineering. It covers a wide range of topics, including numerical integration, random number generation, and applications to option pricing, risk management, and portfolio optimization.
Provides a comprehensive introduction to numerical analysis for applied science. It covers a wide range of topics, including numerical integration. It is suitable for both undergraduate and graduate students.
For more information about how these books relate to this course, visit:
OpenCourser.com/topic/k3t08j/numerical