Your English writing platform
Discover LudwigSuggestions(5)
Exact(25)
During the 1970s Brezis and Browder presented a now classical characterization of maximal monotonicity of monotone linear relations in reflexive spaces.
By the maximal monotonicity of, we have ; consequently,.
By the maximal monotonicity of, we have (3.20).
It follows from the maximal monotonicity of that, that is,.
By virtue of the maximal monotonicity of, we have (3.48).
The maximal monotonicity of is only used to guarantee the existence of solutions of SVME, for any given,, and.
Similar(35)
We introduce a useful extension of the notion of maximal monotonicity (see [14], p.83]).
Finally, Section 4 deals with some important specializations that connect the results on general maximal monotonicity, especially to several aspects of the linear convergence.
The obtained results generalize investigations on general maximal monotonicity and beyond.
The (A, η -accretivity generalizes the general (H, η -accretivity, (I, η)-accretivity (so-called generalizes m-accretheity), H-accretivity classical m-accretivity (A, η)-monotonicity, A-monotonicity, (H, η)-monotonicity, H-monotonicity, maximal η-monotonicity, and classical maximal monotonicity as special cases (see, for example, [1, 7, 8, 13] and the references therein).
Next, we present the super-relaxed Proximal point algorithm based on the maximal monotonicity.
More suggestions(15)
Write better and faster with AI suggestions while staying true to your unique style.
Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com