Titlebook: Numerical Optimization with Computational Errors; Alexander J. Zaslavski Book 2016 Springer International Publishing Switzerland 2016 nonl

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Continuous Subgradient Method,w that our algorithms generate a good approximate solution, if computational errors are bounded from above by a small positive constant. Moreover, for a known computational error, we find out what an approximate solution can be obtained and how much time one needs for this.

Malleable

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Subgradient Projection Algorithm,f convex–concave functions, under the presence of computational errors. We show that our algorithms generate a good approximate solution, if computational errors are bounded from above by a small positive constant. Moreover, for a known computational error, we find out what an approximate solution c

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The Mirror Descent Algorithm,erate a good approximate solution, if computational errors are bounded from above by a small positive constant. Moreover, for a known computational error, we find out what an approximate solution can be obtained and how many iterates one needs for this.

Thyroid-Gland

Gradient Algorithm with a Smooth Objective Function,rs. We show that the algorithm generates a good approximate solution, if computational errors are bounded from above by a small positive constant. Moreover, for a known computational error, we find out what an approximate solution can be obtained and how many iterates one needs for this.

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