2017
DOI: 10.1186/s13660-017-1404-1
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On Hardy-type integral inequalities with the gamma function

Abstract: By means of real analysis and weight functions, we obtain a few equivalent conditions of two kinds of Hardy-type integral inequalities with the non-homogeneous kernel and parameters. The constant factors related to the gamma function are proved to be the best possible. We also consider the operator expressions and some cases of homogeneous kernel.

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Cited by 4 publications
(2 citation statements)
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References 10 publications
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“…The research of Hilbert-type integral inequalities with hybrid kernels is one of the important contents too. The so-called hybrid kernel research is to combine some basic kernels into new integral kernels and do the corresponding research works, which began in 2008 and yielded a lot of results (see previous studies [21][22][23][24][25]. In this paper, by introducing the parameters 1 , 2 , 3 , 4 , the basic kernels k 1 (x, ) = 1 x+ , k 2 (x, ) =…”
Section: Liumentioning
confidence: 99%
See 1 more Smart Citation
“…The research of Hilbert-type integral inequalities with hybrid kernels is one of the important contents too. The so-called hybrid kernel research is to combine some basic kernels into new integral kernels and do the corresponding research works, which began in 2008 and yielded a lot of results (see previous studies [21][22][23][24][25]. In this paper, by introducing the parameters 1 , 2 , 3 , 4 , the basic kernels k 1 (x, ) = 1 x+ , k 2 (x, ) =…”
Section: Liumentioning
confidence: 99%
“…The research of Hilbert‐type integral inequalities with hybrid kernels is one of the important contents too. The so‐called hybrid kernel research is to combine some basic kernels into new integral kernels and do the corresponding research works, which began in 2008 and yielded a lot of results (see previous studies 21‐25 ). In this paper, by introducing the parameters λ 1 , λ 2 , λ 3 , λ 4 , the basic kernels k1false(x,yfalse)=1x+y,k2false(x,yfalse)=||lnyxx+y,k3false(x,yfalse)=maxfalse{x,yfalse},k4false(x,yfalse)=minfalse{x,yfalse} are parametric combined to a mixed kernel as kfalse(x,yfalse):=||lnyxλ1false(minfalse{x,yfalse}false)λ2false(x+yfalse)λ3false(maxfalse{x,yfalse}false)λ4.…”
Section: Introductionmentioning
confidence: 99%