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

arthrodesis

確認(rèn)

輪流

1931-6828 nerally, different. This fact, which was not taken into account in the previous book, is indeed important in practice. For example, the subgradient projection algorithm consists of two steps. The first step is 978-3-030-37824-0978-3-030-37822-6Series ISSN 1931-6828 Series E-ISSN 1931-6836

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不連貫

Subgradient Projection Algorithm,all 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.

激勵(lì)

Lignans

缺陷

Minimization of Quasiconvex Functions,l 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.

積云

https://doi.org/10.1007/978-3-663-07526-4t for every algorithm its iteration consists of several steps and that computational errors for different steps are different, in general. In this chapter we discuss several algorithms which are studied in this book.

細(xì)絲

https://doi.org/10.1007/978-3-030-67572-1rors 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.