Titlebook: Convex Optimization with Computational Errors; Alexander J. Zaslavski Book 2020 Springer Nature Switzerland AG 2020 convex optimization.ma

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虛假

cardiac-arrest

PAEAN

An Optimization Problems with a Composite Objective Function,rors are different. We show that our algorithm generates a good approximate solution, if all the computational errors are bounded from above by a small positive constant. Moreover, if we know the computational errors for the two steps of our algorithm, we find out what approximate solution can be obtained and how many iterates one needs for this.

Commonwealth

A Zero-Sum Game with Two Players,e computational errors are bounded from above by a small positive constant. Moreover, if we know the computational errors for the two steps of our algorithm, we find out what approximate solution can be obtained and how many iterates one needs for this.

ACRID

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Continuous Subgradient Method, that our algorithm generates a good approximate solution, if all the computational errors are bounded from above by a small positive constant. Moreover, if we know the computational errors for the two calculations of our algorithm, we find out what approximate solution can be obtained and how much time one needs for this.

Foregery

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Safety and Epistemic Frankfurt Cases, step is a calculation of a gradient of the objective function while in the second one we calculate a projection on the feasible set. In each of these two steps there is a computational error. In general, these two computational errors are different.

弄臟

https://doi.org/10.1007/978-3-030-67572-1m generates a good approximate solution, if all the computational errors are bounded from above by a small positive constant. Moreover, if we know the computational errors for the two steps of our algorithm, we find out what approximate solution can be obtained and how many iterates one needs for this.