Abstract
The steepest descent method is one of the oldest and well-known search techniques for minimizing multivariable unconstrained optimization problems. This method has played an important role in the development of advanced optimization algorithms. It is a first-order derivative iterative optimization algorithm whose convergence is linear for the case of quadratic functions. If we take steps in the direction of a negative gradient of the function at the given current point to find a local minimum point, then this procedure is called Gradient Descent.
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Mishra, S.K., Ram, B. (2019). Steepest Descent Method. In: Introduction to Unconstrained Optimization with R. Springer, Singapore. https://doi.org/10.1007/978-981-15-0894-3_6
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DOI: https://doi.org/10.1007/978-981-15-0894-3_6
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