Abstract
Unconstrained optimization consists of minimizing a function which depends on a number of real variables without any restrictions on the values of these variables. When the number of variables is large, this problem becomes quite challenging. The most important gradient methods for solving unconstrained optimization problems are described in this chapter. These methods are iterative.
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Andrei, N. (2020). Introduction: Overview of Unconstrained Optimization. In: Nonlinear Conjugate Gradient Methods for Unconstrained Optimization. Springer Optimization and Its Applications, vol 158. Springer, Cham. https://doi.org/10.1007/978-3-030-42950-8_1
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DOI: https://doi.org/10.1007/978-3-030-42950-8_1
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Publisher Name: Springer, Cham
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Online ISBN: 978-3-030-42950-8
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