May 1, 2024
3 minute read
Nonlinear Programming, a subfield of mathematical optimization, is a branch of Applied Mathematics and Computer Science. It delves into optimizing objective functions under a set of constraints, where these objectives and constraints are represented by nonlinear functions. Unlike their linear counterparts, nonlinear functions exhibit a complex relationship, often resulting in non-intuitive behaviors and surfaces. Understanding Nonlinear Programming is particularly valuable in industries such as Engineering, Finance, and Operations Research, where decision-making often involves optimizing complex systems and models.
What is Nonlinear Programming?
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Find a path to becoming a Nonlinear Programming. Learn more at:
OpenCourser.com/topic/u3v3oi/nonlinear
Reading list
We've selected 11 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
Nonlinear Programming.
Provides a comprehensive overview of nonlinear programming, covering both theoretical foundations and practical algorithms. It is well-suited for advanced undergraduates and graduate students in optimization.
Provides a practical introduction to numerical optimization techniques, including those used in nonlinear programming.
Provides a comprehensive treatment of numerical methods for nonlinear optimization, including advanced topics such as large-scale optimization and stochastic programming.
Provides a comprehensive treatment of nonlinear programming theory and algorithms, including advanced topics such as optimality conditions and duality theory.
This textbook provides a comprehensive treatment of nonlinear optimization algorithms and their applications in various fields.
Provides an introduction to nonlinear programming techniques used in chemical engineering, such as process design and optimization.
While this book focuses on convex optimization, much of its content applies to nonlinear programming more generally. Students may begin with this book before transitioning to more specialized works.
Provides a theoretical and practical treatment of nonlinear programming algorithms and their applications in various fields.
Provides an introduction to nonlinear programming techniques used in financial applications, such as portfolio optimization and risk management.
Provides an introduction to nonlinear programming techniques used in artificial intelligence, such as machine learning and computer vision.
Provides a hands-on introduction to nonlinear programming using the Maple software package.
For more information about how these books relate to this course, visit:
OpenCourser.com/topic/u3v3oi/nonlinear
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