May 1, 2024
4 minute read
Numerical optimization is a branch of mathematics that deals with finding the minimum or maximum value of a given function. It is a powerful tool that can be used to solve a wide variety of problems in science, engineering, and business.
Why is numerical optimization important?
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Find a path to becoming a Numerical Optimization. Learn more at:
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Reading list
We've selected 12 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 Optimization.
Provides a comprehensive overview of numerical optimization techniques, including both unconstrained and constrained optimization. It is suitable for both beginners and advanced users.
Focuses on convex optimization, a subfield of numerical optimization that deals with problems where the objective function and constraints are convex. It is suitable for both beginners and advanced users.
Focuses on convex analysis and optimization, providing a comprehensive overview of the theory and applications of convex sets and functions. It is suitable for advanced users.
Provides a comprehensive overview of numerical optimization theory and algorithms, including both unconstrained and constrained optimization. It is suitable for advanced users.
Covers mixed integer nonlinear programming, a subfield of optimization that deals with problems where some of the variables are continuous and some are discrete. It is suitable for advanced users.
Focuses on optimization techniques for data science, covering both theoretical and practical aspects. It is suitable for both beginners and advanced users.
Covers nonlinear programming, a subfield of numerical optimization that deals with problems where the objective function and/or constraints are nonlinear. It is suitable for both beginners and advanced users.
Covers stochastic programming, a subfield of optimization that deals with problems where some of the data is uncertain. It is suitable for advanced users.
Focuses on robust optimization, a subfield of optimization that deals with problems where the data is uncertain and the goal is to find solutions that are robust to these uncertainties. It is suitable for advanced users.
Provides an introduction to applied optimization, focusing on practical applications of optimization techniques in various fields. It is suitable for both beginners and advanced users.
Covers deep learning, a subfield of machine learning that uses artificial neural networks to solve complex problems. It includes a chapter on optimization techniques for deep learning.
Covers optimization methods for large-scale systems, focusing on parallel and distributed optimization algorithms. It is suitable for advanced users.
For more information about how these books relate to this course, visit:
OpenCourser.com/topic/5m9781/numerical
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