1.Introduction
The divided difference of order m for a function f : [a, b] → ℝ at the distinct points x0, . . . , xm ∈ [a, b] is defined recursively, as shown in [2], as follows:
f\left[ {{x_i}} \right] = f\left( {{x_i}} \right),\;\;\;\left( {i = 0, \ldots ,m} \right)
and
f\left[ {{x_0},\ldots ,{x_m}} \right] = {{f\left[ {{x_1}, \ldots ,{x_m}} \right] - f\left[ {{x_0}, \ldots ,{x_{m - 1}}} \right]} \over {{x_m} - {x_0}}}.
The concept of m-convex functions was originally introduced by Popoviciu in [7], providing a foundational framework for the study of generalized convexity. We now present the definition of m-convex function following approach outlined in [6, 16 pp.] and [8, 238 pp.].
Definition 1
A function f : [a, b] → ℝ is said to be m-convex, m ≥ 0 on [a, b] iff for all choices of (m + 1) distinct points in [a, b],
f\left[ {{x_0},\ldots ,{x_m}} \right] \ge 0.
The integral representation for the m−th divided difference over the m-dimensional simplex is well-known and is given as follows (refer to [2]):
(1.1)
f\left[ {{x_0},\ldots ,{x_m}} \right] = \int_{{\Delta _m}} {{f^{\left( m \right)}}\left( {\sum\limits_{j = 0}^m {{u_j}{x_j}} } \right)d{u_0} \ldots d{u_{m - 1}}} ,
where the simplex Δm is defined as
{\Delta _m} = \left\{ {\left( {{u_0},\ldots ,{u_{m - 1}}} \right):{u_j} \ge 0,\sum\limits_{j = 0}^{m - 1} {{u_j} \le 1} } \right\},\;\;\;{u_m} = 1 - \sum\limits_{j = 0}^{m - 1} {{u_j}} ,
assuming the function f has continuous m-th derivative on the interval [a, b]. The formula (1.1) remains valid if some of the nodes x0, . . . , xm are repeated.
From the relation (1.1), it follows that for every function f with a continuous m-th derivative on the interval ⟨a, b⟩,
f\;{\rm{is}}\;m - {\rm{convex}} \Leftrightarrow {f^{\left( m \right)}} \geqslant 0.
The following result, derived using the Schur polynomial and the Vandermonde determinant (extended with a logarithmic function), holds true.
Proposition 1
For monomial function h(x) = xm+k, where k ≥ 1 is an integer, the following holds
h\left[ {{x_0},\ldots ,{x_m}} \right] = \sum\limits_{{j_1} = 0}^m {\sum\limits_{{j_2} = 0}^{{j_1}} \ldots \sum\limits_{{j_k} = 0}^{{j_{k - 1}}} {{x_{{j_1}}}{x_{{j_2}}} \ldots {x_{{j_k}}}} } .
Example 1
In the following sections, we will utilize the integral representation of the divided difference and derive the corresponding integrals. These integrals will be evaluated here using Proposition 1 and basic calculus techniques.
\matrix{ {\int_{{\Delta _m}} {{h^{\left( m \right)}}\left( {\sum\limits_{j = 0}^m {{u_j}{x_j}} } \right)d{u_0} \ldots d{u_{m - 1}}} } \hfill \cr {\;\;\; = \left\{ {\matrix{ {\int_{{\Delta _m}} {d{u_0} \ldots d{u_{m - 1}}} = {1 \over {m!}},\;\;\;\;{\rm{for}}\;\;\;\;h\left( x \right) = {{{x^m}} \over {m!}},} \hfill \cr {\int_{{\Delta _m}} {\sum\nolimits_{j = 0}^m {{u_j}{x_j}d{u_0} \ldots d{u_{m - 1}}} } = {{\sum\nolimits_{j = 0}^m {{x_j}} } \over {\left( {m + 1} \right)!}},\;\;\;\;{\rm{for}}\;\;\;\;h\left( x \right) = {{{x^{m + 1}}} \over {\left( {m + 1} \right)!}}.} \hfill \cr } } \right.} \hfill \cr }
The following Farwig and Zwick’s result in [3] offers an important generalization of Jensen’s inequality specifically tailored for divided differences. This generalization extends the classical form of Jensen’s inequality, adapting it to the context of higher-order differences.
Theorem 1
Let f be (m + 2)-convex on ⟨a, b⟩. Then
G\left( {\rm{x}} \right) = f\left[ {{x_0},\ldots ,{x_m}} \right]
is a convex function of the vector x = (x0, . . . , xm). Consequently,
f\left[ {\sum\limits_{i = 0}^l {{a_i}x_0^i} , \ldots ,\sum\limits_{i = 0}^l {{a_i}x_m^i} } \right] \le \sum\limits_{i = 0}^l {{a_i}f\left[ {x_0^i,\ldots ,x_m^i} \right]} \;\;\;\left( {i\;is\;an\;upper\;index} \right)
holds for all ai ≥ 0, i ∈ {1, . . . , l}, such that
\sum\limits_{i = 0}^l {{a_i}} = 1
.
In [4], the authors developed a refinement of Jensen’s inequality for 4-convex functions. In the next section, we will apply their result to the case of positive weights. For convenience, their result for positive weights is outlined below:
Theorem 2
Let f ∈ C2[ρ1, ρ2] be a 4-convex function and sj ∈ [ρ1, ρ2], uj ≥ 0 for j = 1, 2, . . . , m with
{U_m}: = \sum\nolimits_{j = 1}^m {{u_j}} \ne 0
and
{1 \over {{U_m}}}\sum\nolimits_{j = 1}^m {{u_j}{s_j}} \in \left[ {{\rho _1},{\rho _2}} \right]
. Then we have
(1.2)
\matrix{ {{1 \over {{U_m}}}\sum\limits_{j = 1}^m {{u_j}f\left( {{s_j}} \right)} - f\left( {{1 \over {{U_m}}}\sum\limits_{j = 1}^m {{u_j}{s_j}} } \right)} \hfill \cr {\;\;\;\;\;\; \le {{f''\left( {{\rho _2}} \right) - f''\left( {{\rho _1}} \right)} \over {6\left( {{\rho _2} - {\rho _1}} \right)}}\left( {{1 \over {{U_m}}}\sum\limits_{j = 1}^m {{u_j}s_j^3} - {{\left( {{1 \over {{U_m}}}\sum\limits_{j = 1}^m {{u_j}{s_j}} } \right)}^3}} \right)} \hfill \cr {\;\;\;\;\;\;\;\;\;\;\; + \;{{{\rho _2}f''\left( {{\rho _1}} \right) - {\rho _1}f''\left( {{\rho _2}} \right)} \over {2\left( {{\rho _2} - {\rho _1}} \right)}}\left( {{1 \over {{U_m}}}\sum\limits_{j = 1}^m {{u_j}s_j^2} - {{\left( {{1 \over {{U_m}}}\sum\limits_{j = 1}^m {{u_j}{s_j}} } \right)}^2}} \right).} \hfill \cr }
If f is 4-concave function, then the reverse inequality holds in (1.2).
In the final section, we provide some generalizations for (h, g; α − n)-convex functions, and therefore, we introduce this concept of generalized convexity.
Definition 2
Let h be a non-negative function on J ⊂ ℝ, ⟨0, 1⟩ ⊂ J, h ≠ 0 and let g be a positive function on I ⊂ ℝ and α, n ∈ ⟨0, 1]. A function f : I → ℝ is said to be (h, g; α − n)-convex if it is non-negative and satisfy the following inequality
(1.3)
f\left( {\lambda x + n\left( {1 - \lambda } \right)y} \right) \le h\left( {{\lambda ^\alpha }} \right)f\left( x \right)g\left( x \right) + nh\left( {1 - {\lambda ^\alpha }} \right)f\left( y \right)g\left( y \right)
for all λ ∈ ⟨0, 1⟩ and x, y ∈ I. If (1.3) holds in the reverse sense, then f is said to be (h, g; α − n)-concave function.
This definition extends the concept of an h-convex function, as outlined in the following definition (see [9]).
Definition 3
Let h: J → ℝ be a non-negative function, h ≠ 0. We say that f : I → ℝ is h-convex function if f is non-negative and for all x, y ∈ I, λ ∈ ⟨0, 1⟩ we have
(1.4)
f\left( {\lambda x + \left( {1 - \lambda } \right)y} \right) \le h\left( \lambda \right)f\left( x \right) + h\left( {1 - \lambda } \right)f\left( y \right).
If inequality (1.4) is reversed, then f is said to be h-concave.
2.Extensions of Jensen’s inequality using (m + 4)-convex functions and divided differences
In this paper, we establish an evaluation of Jensen’s inequality for divided differences by utilizing (m + 4)-convex functions. In this way, we provide generalizations of the existing results for divided differences. In Theorem 3, we extend the result from Farwig and Zwick’s Theorem 1, and in Theorem 4, we generalize Theorem 2.62 from [6].
Theorem 3
Let f(m) ∈ C2[ρ1, ρ2] be a (m + 4)-convex function,
{{\bf{s}}^i} = \left( {s_0^i,\ldots ,s_m^i} \right) \in {\left[ {{\rho _1},{\rho _2}} \right]^{m + 1}}
and let ai ≥ 0, i ∈ {0, . . . , l} be such that
\sum\nolimits_{i = 0}^l {{a_i}} = 1
,
{\bar s_j} = \sum\nolimits_{i = 0}^l {{a_i}s_j^i}
, j ∈ {0, . . ., m},
{\bar s_{{j_1}{j_2}}} = \sum\nolimits_{i = 0}^l {{a_i}s_{{j_1}}^is_{{j_2}}^i}
, and
{\bar s_{{j_1}{j_2}{j_3}}} = \sum\nolimits_{i = 0}^l {{a_i}s_{{j_1}}^is_{{j_2}}^is_{{j_3}}^i}
. Then we have
\matrix{ {\sum\limits_{i = 0}^l {{a_i}f\left[ {s_0^i,\ldots ,s_m^i} \right] - f\left[ {{{\bar s}_0},\ldots ,{{\bar s}_m}} \right]} } \hfill \cr {\;\;\;\; \le {{{f^{\left( {m + 2} \right)}}\left( {{\rho _2}} \right) - {f^{\left( {m + 2} \right)}}\left( {{\rho _1}} \right)} \over {{\rho _2} - {\rho _1}}}\left( {\sum\limits_{{j_1} = 0}^m {\sum\limits_{{j_2} = 0}^{{j_1}} {\sum\limits_{{j_3} = 0}^{{j_2}} {\left( {{{\bar s}_{{j_1}{j_2}{j_3}}} - {{\bar s}_{{j_1}}}{{\bar s}_{{j_2}}}{{\bar s}_{{j_3}}}} \right)} } } } \right)} \hfill \cr {\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\; + {{{\rho _2}{f^{\left( {m + 2} \right)}}\left( {{\rho _1}} \right) - {\rho _1}{f^{\left( {m + 2} \right)}}\left( {{\rho _2}} \right)} \over {{\rho _2} - {\rho _1}}}\left( {\sum\limits_{{j_1} = 0}^m {\sum\limits_{{j_2} = 0}^{{j_1}} {\left( {{{\bar s}_{{j_1}{j_2}}} - {{\bar s}_{{j_1}}}{{\bar s}_{{j_2}}}} \right)} } } \right).} \hfill \cr }
Proof
We will begin by utilizing the integral representation of the divided difference, followed by applying inequality (1.2) for the (m + 4)-convex function f(m), and then perform the integration over the simplex. So we have
\matrix{ {\sum\limits_{i = 0}^l {{a_i}f\left[ {s_0^i,\ldots ,s_m^i} \right] - f\left[ {{{\bar s}_0},\ldots ,{{\bar s}_m}} \right]} = \sum\limits_{i = 0}^l {{a_i}\int_{{\Delta _m}} {{f^{\left( m \right)}}\left( {\sum\limits_{j = 0}^m {{u_j}s_j^i} } \right)d{u_0} \ldots d{u_{m - 1}}} } } \hfill \cr {\;\;\; - \int_{{\Delta _m}} {{f^{\left( m \right)}}\left( {\sum\limits_{j = 0}^m {{u_j}} \sum\limits_{i = 0}^l {{a_i}s_j^i} } \right)d{u_0} \ldots d{u_{m - 1}}} } \hfill \cr { = \int_{{\Delta _m}} {\left[ {\sum\limits_{i = 0}^l {{a_i}{f^{\left( m \right)}}\left( {\sum\limits_{j = 0}^m {{u_j}s_j^i} } \right)} - {f^{\left( m \right)}}\left( {\sum\limits_{i = 0}^l {{a_i}} \sum\limits_{j = 0}^m {{u_j}s_j^i} } \right)} \right]d{u_0} \ldots d{u_{m - 1}}} } \hfill \cr { \le \int_{\Delta m} {\left[ {{{{f^{\left( {m + 2} \right)}}\left( {{\rho _2}} \right) - {f^{\left( {m + 2} \right)}}\left( {{\rho _1}} \right)} \over {6\left( {{\rho _2} - {\rho _1}} \right)}}\left( {\sum\limits_{i = 0}^l {{a_i}} {{\left( {\sum\limits_{j = 0}^m {{u_j}s_j^i} } \right)}^3} - {{\left( {\sum\limits_{i = 0}^l {{a_i}} \sum\limits_{j = 0}^m {{u_j}s_j^i} } \right)}^3}} \right)} \right.} } \hfill \cr {\;\;\;\left. { + \;{{{\rho _2}{f^{\left( {m + 2} \right)}}\left( {{\rho _1}} \right) - {\rho _1}{f^{\left( {m + 2} \right)}}\left( {{\rho _2}} \right)} \over {2\left( {{\rho _2} - {\rho _1}} \right)}}\left( {\sum\limits_{i = 0}^l {{a_i}} {{\left( {\sum\limits_{j = 0}^m {{u_j}s_j^i} } \right)}^2} - {{\left( {\sum\limits_{i = 0}^l {{a_i}} \sum\limits_{j = 0}^m {{u_j}s_j^i} } \right)}^2}} \right)} \right]d{u_0} \ldots d{u_{m - 1}}.} \hfill \cr }
By setting
h\left( x \right) = {{{x^{m + 2}}} \over {\left( {m + 2} \right)!}}
in Proposition 1, we compute
\int_{{\Delta _m}} {\sum\limits_{i = 0}^l {{a_i}{{\left( {\sum\limits_{j = 0}^m {{u_j}s_j^i} } \right)}^2}d{u_0} \ldots d{u_{m - 1}}} } \;{\rm{and}}\;\int_{{\Delta _m}} {{{\left( {\sum\limits_{i = 0}^l {{a_i}} \sum\limits_{j = 0}^m {{u_j}s_j^i} } \right)}^2}d{u_0} \ldots d{u_{m - 1}}} .
Similarly, by setting
h\left( x \right) = {{{x^{m + 3}}} \over {\left( {m + 3} \right)!}}
in Proposition 1 we calculate integrals
\int_{{\Delta _m}} {\sum\limits_{i = 0}^l {{a_i}{{\left( {\sum\limits_{j = 0}^m {{u_j}s_j^i} } \right)}^2}d{u_0} \ldots d{u_{m - 1}}} } \;{\rm{and}}\;\int_{{\Delta _m}} {{{\left( {\sum\limits_{i = 0}^l {{a_i}} \sum\limits_{j = 0}^m {{u_j}s_j^i} } \right)}^3}d{u_0} \ldots d{u_{m - 1}}} .
So we have
\matrix{ {\sum\limits_{i = 0}^l {{a_i}f\left[ {s_0^i,\ldots ,s_m^i} \right] - f\left[ {{{\bar s}_0},\ldots ,{{\bar s}_m}} \right]} } \hfill \cr {\;\;\;\;\;\;\;\;\;\;\; \le {{{f^{\left( {m + 2} \right)}}\left( {{\rho _2}} \right) - {f^{\left( {m + 2} \right)}}\left( {{\rho _1}} \right)} \over {\left( {{\rho _2} - {\rho _1}} \right)}}\left( {\sum\limits_{i = 0}^l {{a_i}} \sum\limits_{{j_1} = 0}^m {\sum\limits_{{j_2} = 0}^{{j_1}} {\sum\limits_{{j_3} = 0}^{{j_2}} {s_{{j_1}}^is_{{j_2}}^is_{{j_3}}^i} } } - \sum\limits_{{j_1} = 0}^m {\sum\limits_{{j_2} = 0}^{{j_1}} {\sum\limits_{{j_3} = 0}^{{j_2}} {{{\bar s}_{{j_1}}}{{\bar s}_{{j_2}}}{{\bar s}_{{j_3}}}} } } } \right)} \hfill \cr { + {{{\rho _2}{f^{\left( {m + 2} \right)}}\left( {{\rho _1}} \right) - {\rho _1}{f^{\left( {m + 2} \right)}}\left( {{\rho _2}} \right)} \over {\left( {{\rho _2} - {\rho _1}} \right)}}\left( {\sum\limits_{i = 0}^l {{a_i}} \sum\limits_{{j_1} = 0}^m {\sum\limits_{{j_2} = 0}^{{j_1}} {s_{{j_1}}^is_{{j_2}}^i} } - \sum\limits_{{j_1} = 0}^m {\sum\limits_{{j_2} = 0}^{{j_1}} {{{\bar s}_{{j_1}}}{{\bar s}_{{j_2}}}} } } \right)} \hfill \cr { = {{{f^{\left( {m + 2} \right)}}\left( {{\rho _2}} \right) - {f^{\left( {m + 2} \right)}}\left( {{\rho _1}} \right)} \over {{\rho _2} - {\rho _1}}}\left( {\sum\limits_{{j_1} = 0}^m {\sum\limits_{{j_2} = 0}^{{j_1}} {\sum\limits_{{j_3} = 0}^{{j_2}} {\left( {{{\bar s}_{{j_1}{j_2}{j_3}}} - {{\bar s}_{{j_1}}}{{\bar s}_{{j_2}}}{{\bar s}_{{j_3}}}} \right)} } } } \right)} \hfill \cr {\;\;\;\;\;\;\;\;\;\;\; + {{{\rho _2}{f^{\left( {m + 2} \right)}}\left( {{\rho _1}} \right) - {\rho _1}{f^{\left( {m + 2} \right)}}\left( {{\rho _2}} \right)} \over {{\rho _2} - {\rho _1}}}\left( {\sum\limits_{{j_1} = 0}^m {\sum\limits_{{j_2} = 0}^{{j_1}} {\left( {{{\bar s}_{{j_1}{j_2}}} - {{\bar s}_{{j_1}}}{{\bar s}_{{j_2}}}} \right)} } } \right).} \hfill \cr }
The following theorem presents a generalization of the result from Theorem 2.62 in [6]:
Theorem 4
Let f(m) ∈ C2[ρ1, ρ2] be a (m + 4)-convex function and sk ∈ [ρ1, ρ2]. Then we have
\matrix{ {{{\sum\nolimits_{j = 0}^m {{f^{\left( m \right)}}\left( {{s_j}} \right)} } \over {\left( {m + 1} \right)!}} - f\left[ {{s_0},\ldots ,{s_m}} \right]} \hfill \cr {\;\;\;\;\; \le {{{f^{\left( {m + 2} \right)}}\left( {{\rho _2}} \right) - {f^{\left( {m + 2} \right)}}\left( {{\rho _1}} \right)} \over {6\left( {{\rho _2} - {\rho _1}} \right)}}\left( {{{\sum\nolimits_{j = 0}^m {s_j^3} } \over {\left( {m + 1} \right)!}} - 6\sum\limits_{{j_1} = 0}^m {\sum\limits_{{j_2} = 0}^{{j_1}} {\sum\limits_{{j_3} = 0}^{{j_2}} {{s_{{j_1}}}{s_{{j_2}}}{s_{{j_3}}}} } } } \right)} \hfill \cr {\;\;\;\;\;\;\;\;\;\;\;\;\; + \;{{{\rho _2}{f^{\left( {m + 2} \right)}}\left( {{\rho _1}} \right) - {\rho _1}{f^{\left( {m + 2} \right)}}\left( {{\rho _2}} \right)} \over {2\left( {{\rho _2} - {\rho _1}} \right)}}\left( {{{\sum\nolimits_{j = 0}^m {s_j^2} } \over {\left( {m + 1} \right)!}} - 2\sum\limits_{{j_1} = 0}^m {\sum\limits_{{j_2} = 0}^{{j_1}} {{s_{{j_1}}}{s_{{j_2}}}} } } \right).} \hfill \cr }
Proof
Similarly as in Theorem 3, we have:
\matrix{ {\int_{{\Delta _m}} {\sum\limits_{j = 0}^m {{u_j}{f^{\left( m \right)}}\left( {{s_j}} \right)d{u_0} \ldots d{u_{m - 1}}} } - \int_{{\Delta _m}} {{f^{\left( m \right)}}\left( {\sum\limits_{j = 0}^m {{u_j}{s_j}} } \right)d{u_0} \ldots d{u_{m - 1}}} } \hfill \cr {\;\;\;\;\;\;\; \le {{{f^{\left( {m + 2} \right)}}\left( {{\rho _2}} \right) - {f^{\left( {m + 2} \right)}}\left( {{\rho _1}} \right)} \over {6\left( {{\rho _2} - {\rho _1}} \right)}}\left( {\int_{{\Delta _m}} {\sum\limits_{j = 0}^m {{u_j}s_j^3d{u_0} \ldots d{u_{m - 1}}} } - \int_{{\Delta _m}} {{{\left( {\sum\limits_{j = 0}^m {{u_j}{s_j}} } \right)}^3}d{u_0} \ldots d{u_{m - 1}}} } \right)} \hfill \cr {\;\;\;\; + \;{{{\rho _2}{f^{\left( {m + 2} \right)}}\left( {{\rho _1}} \right) - {\rho _1}{f^{\left( {m + 2} \right)}}\left( {{\rho _2}} \right)} \over {2\left( {{\rho _2} - {\rho _1}} \right)}}\left( {\int_{\Delta m} {\sum\limits_{j = 0}^m {{u_j}s_j^2d{u_0} \ldots d{u_{m - 1}}} } - \int_{{\Delta _m}} {{{\left( {\sum\limits_{j = 0}^m {{u_j}{s_j}} } \right)}^2}d{u_0} \ldots d{u_{m - 1}}} } \right).} \hfill \cr }
We apply Example 1 to compute the following integrals:
\matrix{ {\int_{{\Delta _m}} {\sum\limits_{j = 0}^m {{u_j}{f^{\left( m \right)}}\left( {{s_j}} \right)d{u_0} \ldots d{u_{m - 1}}} } ,\;\;\;\int_{{\Delta _m}} {\sum\limits_{j = 0}^m {{u_j}s_j^3d{u_0} \ldots d{u_{m - 1}}} } ,} \hfill \cr {\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;{\rm{and}}\;\;\;\int_{{\Delta _m}} {\sum\limits_{j = 0}^m {{u_j}s_j^2d{u_0} \ldots d{u_{m - 1}}} } .} \hfill \cr }
Next, by setting
h\left( x \right) = {{{x^{m + 2}}} \over {\left( {m + 2} \right)!}}\;\;\;{\rm{and}}\;\;\;h\left( x \right) = {{{x^{m + 3}}} \over {\left( {m + 3} \right)!}}
in Proposition 1, we compute the corresponding expression
\int_{{\Delta _m}} {{{\left( {\sum\limits_{j = 0}^m {{u_j}{s_j}} } \right)}^2}d{u_0} \ldots d{u_{m - 1}}} \;\;\;{\rm{and}}\;\;\;\int_{{\Delta _m}} {{{\left( {\sum\limits_{j = 0}^m {{u_j}{s_j}} } \right)}^3}d{u_0} \ldots d{u_{m - 1}}} .
So we have
\matrix{ {{{\sum\nolimits_{j = 0}^m {{f^{\left( m \right)}}\left( {{s_j}} \right)} } \over {\left( {m + 1} \right)!}} - f\left[ {{s_0},\ldots ,{s_m}} \right]} \hfill \cr {\;\;\;\;\; \le {{{f^{\left( {m + 2} \right)}}\left( {{\rho _2}} \right) - {f^{\left( {m + 2} \right)}}\left( {{\rho _1}} \right)} \over {6\left( {{\rho _2} - {\rho _1}} \right)}}\left( {{{\sum\nolimits_{j = 0}^m {s_j^3} } \over {\left( {m + 1} \right)!}} - 6\sum\limits_{{j_1} = 0}^m {\sum\limits_{{j_2} = 0}^{{j_1}} {\sum\limits_{{j_3} = 0}^{{j_2}} {{s_{{j_1}}}{s_{{j_2}}}{s_{{j_3}}}} } } } \right)} \hfill \cr {\;\;\;\;\;\;\; + \;{{{\rho _2}{f^{\left( {m + 2} \right)}}\left( {{\rho _1}} \right) - {\rho _1}{f^{\left( {m + 2} \right)}}\left( {{\rho _2}} \right)} \over {2\left( {{\rho _2} - {\rho _1}} \right)}}\left( {{{\sum\nolimits_{j = 0}^m {s_j^2} } \over {\left( {m + 1} \right)!}} - 2\sum\limits_{{j_1} = 0}^m {\sum\limits_{{j_2} = 0}^{{j_1}} {{s_{{j_1}}}{s_{{j_2}}}} } } \right).} \hfill \cr }
The following theorem extends inequalities related to divided differences for (h, g; α − n)-convex functions.
Theorem 5
Let f(m) be a nonnegative (h, g; α − n)-convex function on [0,∞⟩ where h is a nonnegative function on J ⊂ ℝ, h ≠ 0, g is positive function on [0,∞⟩, α, n ∈ ⟨0, 1], 0 ≤ ρ1 < ρ2 < ∞, f(m), g, h ∈ L1[ρ1, ρ2] and ai ≥ 0, i ∈ {0, . . . , l} such that
\sum\nolimits_{i = 0}^l {{a_i} = 1}
. Then the following inequality holds
(2.1)
\matrix{ {\sum\limits_{i = 0}^l {{a_i}f\left[ {s_0^i,\ldots ,s_m^i} \right]} } \hfill \cr {\;\;\; \le \sum\limits_{i = 0}^l {{a_i}} \int_{{\Delta _m}} {{{\min}}\left\{ {\left[ {h\left( {{{\left( {{{{\rho _2}n - \sum\nolimits_{j = 0}^m {{u_j}s_j^i} } \over {{\rho _2}n - {\rho _1}}}} \right)}^\alpha }} \right){f^{\left( m \right)}}\left( {{\rho _1}} \right)g\left( {{\rho _1}} \right)} \right.} \right.} } \hfill \cr {\;\;\;\;\;\;\;\;\; + \left. {\;nh\left( {1 - {{\left( {{{{\rho _2}n - \sum\nolimits_{j = 0}^m {{u_j}s_j^i} } \over {{\rho _2}n - {\rho _1}}}} \right)}^\alpha }} \right){f^{\left( m \right)}}\left( {{\rho _2}} \right)g\left( {{\rho _2}} \right)} \right],} \hfill \cr {\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\left[ {h\left( {{{\left( {{{\sum\nolimits_{j = 0}^m {{u_j}s_j^i - n{\rho _1}} } \over {{\rho _2} - n{\rho _1}}}} \right)}^\alpha }} \right){f^{\left( m \right)}}\left( {{\rho _2}} \right)g\left( {{\rho _2}} \right)} \right.} \hfill \cr {\left. {\;\;\;\;\;\; + \;nh\left. {\left( {1 - {{\left( {{{\sum\nolimits_{j = 0}^m {{u_j}s_j^i - n{\rho _1}} } \over {{\rho _2} - n{\rho _1}}}} \right)}^\alpha }} \right){f^{\left( m \right)}}\left( {{\rho _1}} \right)g\left( {{\rho _1}} \right)} \right]} \right\}d{u_0} \ldots d{u_{m - 1}}} \hfill \cr {\;\;\; \le \sum\limits_{i = 0}^l {{a_i}} \min \left\{ {\int_{{\Delta _m}} {\left[ {h\left( {{{\left( {{{{\rho _2}n - \sum\nolimits_{j = 0}^m {{u_j}s_j^i} } \over {{\rho _2}n - {\rho _1}}}} \right)}^\alpha }} \right){f^{\left( m \right)}}\left( {{\rho _1}} \right)g\left( {{\rho _1}} \right)} \right.} } \right.} \hfill \cr {\left. {\;\;\;\;\;\;\;\;\;\;\; + \;nh\left. {\left( {1 - {{\left( {{{{\rho _2}n - \sum\nolimits_{j = 0}^m {{u_j}s_j^i} } \over {{\rho _2}n - {\rho _1}}}} \right)}^\alpha }} \right){f^{\left( m \right)}}\left( {{\rho _2}} \right)g\left( {{\rho _2}} \right)} \right]d{u_0} \ldots d{u_{m - 1}}} \right\}.} \hfill \cr {\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\int_{{\Delta _m}} {\left[ {h\left( {{{\left( {{{\sum\nolimits_{j = 0}^m {{u_j}s_j^i} - n{\rho _1}} \over {{\rho _2} - n{\rho _1}}}} \right)}^\alpha }} \right){f^{\left( m \right)}}\left( {{\rho _2}} \right)g\left( {{\rho _2}} \right)} \right.} } \hfill \cr {\left. {\;\;\;\;\;\;\;\;\;\;\;\; + \;nh\left. {\left( {1 - {{\left( {{{\sum\nolimits_{j = 0}^m {{u_j}s_j^i - n{\rho _1}} } \over {{\rho _2} - n{\rho _1}}}} \right)}^\alpha }} \right){f^{\left( m \right)}}\left( {{\rho _1}} \right)g\left( {{\rho _1}} \right)} \right]d{u_0} \ldots d{u_{m - 1}}} \right\}.} \hfill \cr }
Proof
First, we express the divided difference in its integral form. Next, we will prove an auxiliary result using Definition 2. To do so, we apply Definition 2 and set
u = \lambda x + n\left( {1 - \lambda } \right)y,
and then solve for
\lambda = {{u - ny} \over {x - ny}}.
Substituting this into the inequality (1.3), we obtain
f\left( u \right) \le h\left( {{{\left( {{{u - ny} \over {x - ny}}} \right)}^\alpha }} \right)f\left( x \right)g\left( x \right) + nh\left( {1 - {{\left( {{{u - ny} \over {x - ny}}} \right)}^\alpha }} \right)f\left( y \right)g\left( y \right).
By swapping x and y, we obtain
\lambda = {{u - nx} \over {y - nx}},
which leads to
f\left( u \right) \le h\left( {{{\left( {{{u - nx} \over {y - nx}}} \right)}^\alpha }} \right)f\left( y \right)g\left( y \right) + nh\left( {1 - {{\left( {{{u - nx} \over {y - nx}}} \right)}^\alpha }} \right)f\left( x \right)g\left( x \right).
Thus, we derive the following inequality:
(2.2)
\matrix{ {f\left( u \right) \le \min \left\{ {h\left( {{{\left( {{{u - ny} \over {x - ny}}} \right)}^\alpha }} \right)f\left( x \right)g\left( x \right) + nh\left( {1 - {{\left( {{{u - ny} \over {x - ny}}} \right)}^\alpha }} \right)f\left( y \right)g\left( y \right)} \right.,} \cr {\left. {h\left( {{{\left( {{{u - nx} \over {y - nx}}} \right)}^\alpha }} \right)f\left( y \right)g\left( y \right) + nh\left( {1 - {{\left( {{{u - nx} \over {y - nx}}} \right)}^\alpha }} \right)f\left( x \right)g\left( x \right)} \right\}.} \cr }
We apply (2.2) to the function f(m), and integrating over the simplex, while utilizing the simple fact that
\int_{{\Delta _m}} {\min \left\{ {f,g} \right\}} \; \le \min \left\{ {\int_{{\Delta _m}} f ,\int_{{\Delta _m}} g } \right\},
we obtain the desired result (2.1)
3.Applications
In the special case of Theorem 5 when h(x) = x, α = 1, n = 1, we obtain the following result for what we will refer to as g-convex functions:
Corollary 1
Let f(m) be a nonnegative function on [0,∞⟩, g is positive function on [0,∞⟩, 0 ≤ ρ1 < ρ2 < ∞ and f(m), g ∈ L1[ρ1, ρ2], ai ≥ 0, i ∈ {0, . . . , l} such that
\sum\nolimits_{i = 0}^l {{a_i} = 0}
and
{\bar s_j} = \sum\nolimits_{i = 0}^l {{a_i}s_j^i}
. Then the following inequality holds
(3.1)
\matrix{ {m!\sum\limits_{i = 0}^l {{a_i}f\left[ {s_0^i,\ldots ,s_m^i} \right]} \le {{{f^{\left( m \right)}}\left( {{\rho _1}} \right)g\left( {{\rho _1}} \right)} \over {{\rho _2} - {\rho _1}}}\left( {{\rho _2} - {1 \over {m + 1}}\sum\limits_{j = 0}^m {{{\bar s}_j}} } \right)} \hfill \cr {\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\; + \;{{{f^{\left( m \right)}}\left( {{\rho _2}} \right)g\left( {{\rho _2}} \right)} \over {{\rho _2} - {\rho _1}}}\left( {{1 \over {m + 1}}\sum\limits_{j = 0}^m {\bar s - {\rho _1}} } \right).} \hfill \cr }
Proof
We derive the following from (2.1):
\matrix{ {\sum\limits_{i = 0}^l {{a_i}f\left[ {s_0^i,\ldots ,s_m^i} \right]} = \sum\limits_{i = 0}^l {{a_i}\int_{{\Delta _m}} {{f^{\left( m \right)}}\left( {\sum\limits_{j = 0}^m {{u_j}s_j^i} } \right)d{u_0} \ldots d{u_{m - 1}}} } } \hfill \cr {\; \le \sum\limits_{i = 0}^l {{a_i}} \min \left\{ {\int_{{\Delta _m}} {\left[ {{{{\rho _2} - \sum\nolimits_{j = 0}^m {{u_j}s_j^i} } \over {{\rho _2} - {\rho _1}}}{f^{\left( m \right)}}\left( {{\rho _1}} \right)g\left( {{\rho _1}} \right) + {{\sum\nolimits_{j = 0}^m {{u_j}s_j^i - {\rho _1}} } \over {{\rho _2} - {\rho _1}}}} \right.} } \right.} \hfill \cr {\left. {\;\;\;\; \times \;{f^{\left( m \right)}}\left( {{\rho _2}} \right)g\left( {{\rho _2}} \right)} \right]d{u_0} \ldots d{u_{m - 1}},\int_{{\Delta _m}} {\left[ {{{\sum\nolimits_{j = 0}^m {{u_j}s_j^i - {\rho _1}} } \over {{\rho _2} - {\rho _1}}}{f^{\left( m \right)}}\left( {{\rho _2}} \right)g\left( {{\rho _2}} \right)} \right.} } \hfill \cr {\left. {\left. {\;\;\;\; + \;{{{\rho _2} - \sum\nolimits_{j = 0}^m {{u_j}s_j^i} } \over {{\rho _2} - {\rho _1}}}{f^{\left( m \right)}}\left( {{\rho _1}} \right)g\left( {{\rho _1}} \right)} \right]d{u_0} \ldots d{u_{m - 1}}} \right\}} \hfill \cr {\; = \sum\limits_{i = 0}^l {{a_i}} \min \left\{ {{{\left( {m + 1} \right){\rho _2} - \sum\nolimits_{j = 0}^m {s_j^i} } \over {\left( {m + 1} \right)!\left( {{\rho _2} - {\rho _1}} \right)}}{f^{\left( m \right)}}\left( {{\rho _1}} \right)g\left( {{\rho _1}} \right)} \right.} \hfill \cr {\;\;\;\; + \;{{\sum\nolimits_{j = 0}^m {s_j^i} - {\rho _1}\left( {m + 1} \right)} \over {\left( {m + 1} \right)!\left( {{\rho _2} - {\rho _1}} \right)}}{f^{\left( m \right)}}\left( {{\rho _2}} \right)g\left( {{\rho _2}} \right),{{\sum\nolimits_{j = 0}^m {s_j^i} - {\rho _1}\left( {m + 1} \right)} \over {\left( {m + 1} \right)!\left( {{\rho _2} - {\rho _1}} \right)}}{f^{\left( m \right)}}\left( {{\rho _2}} \right)g\left( {{\rho _2}} \right)} \hfill \cr {\;\;\;\;\left. { + \;{{\left( {m + 1} \right){\rho _2} - \sum\nolimits_{j = 0}^m {s_j^i} } \over {\left( {m + 1} \right)!\left( {{\rho _2} - {\rho _1}} \right)}}{f^{\left( m \right)}}\left( {{\rho _1}} \right)g\left( {{\rho _1}} \right)} \right\}} \hfill \cr { = {{\left( {m + 1} \right){\rho _2} - \sum\nolimits_{j = 0}^m {{{\bar s}_j}} } \over {\left( {m + 1} \right)!\left( {{\rho _2} - {\rho _1}} \right)}}{f^{\left( m \right)}}\left( {{\rho _1}} \right)g\left( {{\rho _1}} \right) + {{\sum\nolimits_{j = 0}^m {{{\bar s}_j}} - {\rho _1}\left( {m + 1} \right)} \over {\left( {m + 1} \right)!\left( {{\rho _2} - {\rho _1}} \right)}}{f^{\left( m \right)}}\left( {{\rho _2}} \right)g\left( {{\rho _2}} \right).} \hfill \cr }
With some elementary calculations and rearrangement, we arrive at (3.1).
Remark 1
In the special case where h(x) = x, α = 1, n = 1, g ≡ 1, we obtain the result for convex functions, and the following Lah-Ribarič inequality holds, as proven [1, Theorem 10].
\matrix{ {\sum\limits_{i = 0}^l {{a_i}f\left[ {s_0^i,\ldots ,s_m^i} \right]} = \sum\limits_{i = 0}^l {{a_i}} \int_{{\Delta _m}} {{f^{\left( m \right)}}\left( {\sum\limits_{j = 0}^m {{u_j}s_j^i} } \right)d{u_0} \ldots d{u_{m - 1}}} } \hfill \cr { \le \sum\limits_{i = 0}^l {{a_i}} \min \left\{ {\int_{{\Delta _m}} {\left[ {{{{\rho _2} - \sum\nolimits_{j = 0}^m {{u_j}s_j^i} } \over {{\rho _2} - {\rho _1}}}{f^{\left( m \right)}}\left( {{\rho _1}} \right)} \right.} } \right.} \hfill \cr {\left. {\;\;\; + \;{{\sum\nolimits_{j = 0}^m {{u_j}s_j^i} - {\rho _1}} \over {{\rho _2} - {\rho _1}}}{f^{\left( m \right)}}\left( {{\rho _2}} \right)} \right]d{u_0} \ldots d{u_{m - 1}},\int_{{\Delta _m}} {\left[ {{{\sum\nolimits_{j = 0}^m {{u_j}s_j^i} - {\rho _1}} \over {{\rho _2} - {\rho _1}}}{f^{\left( m \right)}}\left( {{\rho _2}} \right)} \right.} } \hfill \cr {\;\;\;\left. { + \left. {\;{{{\rho _2} - \sum\nolimits_{j = 0}^m {{u_j}s_j^i} } \over {{\rho _2} - {\rho _1}}}{f^{\left( m \right)}}\left( {{\rho _1}} \right)} \right]d{u_0} \ldots d{u_{m - 1}}} \right\}} \hfill \cr { = {{\left( {m + 1} \right){\rho _2} - \sum\nolimits_{j = 0}^m {{{\bar s}_j}} } \over {\left( {m + 1} \right)!\left( {{\rho _2} - {\rho _1}} \right)}}{f^{\left( m \right)}}\left( {{\rho _1}} \right) + {{\sum\nolimits_{j = 0}^m {{{\bar s}_j}} - {\rho _1}\left( {m + 1} \right)} \over {\left( {m + 1} \right)!\left( {{\rho _2} - {\rho _1}} \right)}}{f^{\left( m \right)}}\left( {{\rho _2}} \right).} \hfill \cr }
In the following theorem, we apply the results obtained to the function F of two variables, as described by Pečarić and Beesack in [5] (see also [6]).
Theorem 6
Let f(m) be a nonnegative function on [0,∞⟩, g is positive function on [0,∞⟩, 0 ≤ ρ1 < ρ2 < ∞ and f(m), g ∈ L1[ρ1, ρ2], ai ≥ 0, i ∈ {0, . . . , l} such that
\sum\nolimits_{i = 0}^l {{a_i} = 1}
and
{\bar s_j} = \sum\nolimits_{i = 0}^l {{a_i}s_j^i}
and let J be an interval such that J ⊃ f(m)(I). If F : J × J → ℝ is a function defined such that u ↦ F(u, v) is increasing for any v ∈ J, then for every
\xi = {1 \over {m + 1}}\sum\nolimits_{j = 0}^m {{{\bar s}_j}}
we have
\matrix{ {F\left( {m!\sum\limits_{i = 0}^l {{a_i}f\left[ {s_0^i,\ldots ,s_m^i} \right]} ,{f^{\left( m \right)}}\left( \xi \right)} \right)} \hfill \cr {\; \le F\left( {{{{f^{\left( m \right)}}\left( {{\rho _1}} \right)g\left( {{\rho _1}} \right)} \over {{\rho _2} - {\rho _1}}}\left( {{\rho _2} - \xi } \right) + {{{f^{\left( m \right)}}\left( {{\rho _2}} \right)g\left( {{\rho _2}} \right)} \over {{\rho _2} - {\rho _1}}}\left( {\xi - {\rho _1}} \right),{f^{\left( m \right)}}\left( \xi \right)} \right)} \hfill \cr {\; \le \mathop {\max }\limits_{\xi \in \left[ {{\rho _1},{\rho _2}} \right]} \;F\left( {{{{f^{\left( m \right)}}\left( {{\rho _1}} \right)g\left( {{\rho _1}} \right)} \over {{\rho _2} - {\rho _1}}}\left( {{\rho _2} - \xi } \right) + {{{f^{\left( m \right)}}\left( {{\rho _2}} \right)g\left( {{\rho _2}} \right)} \over {{\rho _2} - {\rho _1}}}\left( {\xi - {\rho _1}} \right),{f^{\left( m \right)}}\left( \xi \right)} \right).} \hfill \cr }
In the special case of Theorem 5 when α = 1, n = 1, we obtain the following result for (h, g)-convex functions:
Corollary 2
Let f(m) be a nonnegative (h, g)-convex function on [0,∞⟩ where h is a nonnegative concave function on J ⊂ ℝ, h ≠ 0, g is positive function on [0,∞⟩, 0 ≤ ρ1 < ρ2 < ∞, f(m), g, h ∈ L1[ρ1, ρ2] and ai ≥ 0, i ∈ {0, . . . , l} such that
\sum\nolimits_{i = 0}^l {{a_i} = 1}
. Then the following inequality holds
\matrix{ {m!\sum\limits_{i = 0}^l {{a_i}f\left[ {s_0^i,\ldots ,s_m^i} \right]} \le \sum\limits_{i = 0}^l {{a_i}\left[ {{f^{\left( m \right)}}\left( {{\rho _1}} \right)g\left( {{\rho _1}} \right)h\left( {{{{\rho _2} - {1 \over {m + 1}}\sum\nolimits_{j = 0}^m {s_j^i} } \over {{\rho _2} - {\rho _1}}}} \right)} \right.} } \hfill \cr {\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\left. { + \;{f^{\left( m \right)}}\left( {{\rho _2}} \right)g\left( {{\rho _2}} \right)h\left( {{{{1 \over {m + 1}}\sum\nolimits_{j = 0}^m {s_j^i} - {\rho _1}} \over {{\rho _2} - {\rho _1}}}} \right)} \right].} \hfill \cr }
Proof
\matrix{ {\sum\limits_{i = 0}^l {{a_i}f\left[ {s_0^i,\ldots ,s_m^i} \right]} \le \sum\limits_{i = 0}^l {{a_i}} \int_{{\Delta _m}} {\min \left\{ {\left[ {h\left( {{{{\rho _2} - \sum\nolimits_{j = 0}^m {{u_j}s_j^i} } \over {{\rho _2} - {\rho _1}}}} \right){f^{\left( m \right)}}\left( {{\rho _1}} \right)g\left( {{\rho _1}} \right)} \right.} \right.} } \hfill \cr {\left. {\;\;\; + \;h\left( {{{\sum\nolimits_{j = 0}^m {{u_j}s_j^i} - {\rho _1}} \over {{\rho _2} - {\rho _1}}}} \right){f^{\left( m \right)}}\left( {{\rho _2}} \right)g\left( {{\rho _2}} \right)} \right],\left[ {h\left( {{{\sum\nolimits_{j = 0}^m {{u_j}s_j^i} - {\rho _1}} \over {{\rho _2} - {\rho _1}}}} \right){f^{\left( m \right)}}\left( {{\rho _2}} \right)g\left( {{\rho _2}} \right)} \right.} \hfill \cr {\left. {\left. {\;\;\; + \;h\left( {{{{\rho _2} - \sum\nolimits_{j = 0}^m {{u_j}s_j^i} } \over {{\rho _2} - {\rho _1}}}} \right){f^{\left( m \right)}}\left( {{\rho _1}} \right)g\left( {{\rho _1}} \right)} \right]} \right\}d{u_0} \ldots d{u_{m - 1}}} \hfill \cr { \le \sum\limits_{i = 0}^l {{a_i}} \min \left\{ {\int_{{\Delta _m}} {\left[ {h\left( {{{{\rho _2} - \sum\nolimits_{j = 0}^m {{u_j}s_j^i} } \over {{\rho _2} - {\rho _1}}}} \right){f^{\left( m \right)}}\left( {{\rho _1}} \right)g\left( {{\rho _1}} \right)} \right.} } \right.} \hfill \cr {\left. {\;\;\; + \;h\left( {{{\sum\nolimits_{j = 0}^m {{u_j}s_j^i} - {\rho _1}} \over {{\rho _2} - {\rho _1}}}} \right){f^{\left( m \right)}}\left( {{\rho _2}} \right)g\left( {{\rho _2}} \right)} \right]d{u_0} \ldots d{u_{m - 1}},} \hfill \cr {\;\;\;\int_{{\Delta _m}} {\left[ {h\left( {{{\sum\nolimits_{j = 0}^m {{u_j}s_j^i} - {\rho _1}} \over {{\rho _2} - {\rho _1}}}} \right){f^{\left( m \right)}}\left( {{\rho _2}} \right)g\left( {{\rho _2}} \right)} \right.} } \hfill \cr {\left. {\left. { + \;h\left( {{{{\rho _2} - \sum\nolimits_{j = 0}^m {{u_j}s_j^i} } \over {{\rho _2} - {\rho _1}}}} \right){f^{\left( m \right)}}\left( {{\rho _1}} \right)g\left( {{\rho _1}} \right)} \right]d{u_0} \ldots d{u_{m - 1}}} \right\}} \hfill \cr { \le \sum\limits_{i = 0}^l {{a_i}} \min \left\{ {\left[ {{{{f^{\left( m \right)}}\left( {{\rho _1}} \right)g\left( {{\rho _1}} \right)} \over {m!}}h\left( {m!\int_{{\Delta _m}} {{{{\rho _2} - \sum\nolimits_{j = 0}^m {{u_j}s_j^i} } \over {{\rho _2} - {\rho _1}}}d{u_0} \ldots d{u_{m - 1}}} } \right)} \right.} \right.} \hfill \cr {\left. {\;\;\; + \;{{{f^{\left( m \right)}}\left( {{\rho _2}} \right)g\left( {{\rho _2}} \right)} \over {m!}}h\left( {m!\int_{{\Delta _m}} {{{\sum\nolimits_{j = 0}^m {{u_j}s_j^i} - {\rho _1}} \over {{\rho _2} - {\rho _1}}}d{u_0} \ldots d{u_{m - 1}}} } \right)} \right],} \hfill \cr {\;\;\;\left[ {{{{f^{\left( m \right)}}\left( {{\rho _2}} \right)g\left( {{\rho _2}} \right)} \over {m!}}h\left( {m!\int_{{\Delta _m}} {{{\sum\nolimits_{j = 0}^m {{u_j}s_j^i} - {\rho _1}} \over {{\rho _2} - {\rho _1}}}d{u_0} \ldots d{u_{m - 1}}} } \right)} \right.} \hfill \cr {\left. {\left. {\;\;\; + \;{{{f^{\left( m \right)}}\left( {{\rho _1}} \right)g\left( {{\rho _1}} \right)} \over {m!}}h\left( {m!\int_{{\Delta _m}} {{{{\rho _2} - \sum\nolimits_{j = 0}^m {{u_j}s_j^i} } \over {{\rho _2} - {\rho _1}}}d{u_0} \ldots d{u_{m - 1}}} } \right)} \right]} \right\}} \hfill \cr { = \sum\limits_{i = 0}^l {{a_i}} \min \left\{ {\left[ {{{{f^{\left( m \right)}}\left( {{\rho _1}} \right)g\left( {{\rho _1}} \right)} \over {m!}}h\left( {{{{\rho _2} - {1 \over {m + 1}}\sum\nolimits_{j = 0}^m {s_j^i} } \over {{\rho _2} - {\rho _1}}}} \right)} \right.} \right.} \hfill \cr {\left. {\;\;\; + \;{{{f^{\left( m \right)}}\left( {{\rho _2}} \right)g\left( {{\rho _2}} \right)} \over {m!}}h\left( {{{{1 \over {m + 1}}\sum\nolimits_{j = 0}^m {s_j^i} - {\rho _1}} \over {{\rho _2} - {\rho _1}}}} \right)} \right],} \hfill \cr {\left. {\left[ {{{{f^{\left( m \right)}}\left( {{\rho _2}} \right)g\left( {{\rho _2}} \right)} \over {m!}}h\left( {{{{1 \over {m + 1}}\sum\nolimits_{j = 0}^m {s_j^i} - {\rho _1}} \over {{\rho _2} - {\rho _1}}}} \right) + {{{f^{\left( m \right)}}\left( {{\rho _1}} \right)g\left( {{\rho _1}} \right)} \over {m!}}h\left( {{{{\rho _2} - {1 \over {m + 1}}\sum\nolimits_{j = 0}^m {s_j^i} } \over {{\rho _2} - {\rho _1}}}} \right)} \right]} \right\}} \hfill \cr { = \sum\limits_{i = 0}^l {{a_i}\left[ {{{{f^{\left( m \right)}}\left( {{\rho _1}} \right)g\left( {{\rho _1}} \right)} \over {m!}}h\left( {{{{\rho _2} - {1 \over {m + 1}}\sum\nolimits_{j = 0}^m {s_j^i} } \over {{\rho _2} - {\rho _1}}}} \right)} \right.} } \hfill \cr {\;\;\;\left. { + \;{{{f^{\left( m \right)}}\left( {{\rho _2}} \right)g\left( {{\rho _2}} \right)} \over {m!}}h\left( {{{{1 \over {m + 1}}\sum\nolimits_{j = 0}^m {s_j^i} - {\rho _1}} \over {{\rho _2} - {\rho _1}}}} \right)} \right].} \hfill \cr}
Theorem 7
Let f(m) be a nonnegative (h, g)-convex function on [0,∞⟩ where h is a nonnegative concave function on J ⊂ ℝ, h ≠ 0, g is positive function on [0,∞⟩, 0 ≤ ρ1 < ρ2 < ∞ and f(m), g, h ∈ L1[ρ1, ρ2], ai ≥ 0, i ∈ {0, . . . , l} such that
\sum\nolimits_{i = 0}^l {{a_i} = 1}
and let J be an interval such that J ⊃ f(m)(I). If F : J × J → ℝ is a function defined such that u ↦ F(u, v) is increasing for any v ∈ J, then for every
{\xi ^i} = {1 \over {m + 1}}\sum\nolimits_{j = 0}^m {s_j^i}
we have
\matrix{ {F\left( {m!\sum\limits_{i = 0}^l {{a_i}f\left[ {s_0^i,\ldots ,s_m^i} \right]} ,{f^{\left( m \right)}}\left( {{\xi ^i}} \right)} \right)} \hfill \cr { \le F\left[ {\sum\limits_{i = 0}^l {{a_i}} \left( {{f^{\left( m \right)}}\left( {{\rho _1}} \right)g\left( {{\rho _1}} \right)h\left( {{{{\rho _2} - {\xi ^i}} \over {{\rho _2} - {\rho _1}}}} \right) + {f^{\left( m \right)}}\left( {{\rho _2}} \right)g\left( {{\rho _2}} \right)h\left( {{{{\xi ^i} - {\rho _1}} \over {{\rho _2} - {\rho _1}}}} \right)} \right),{f^{\left( m \right)}}\left( {{\xi ^i}} \right)} \right]} \hfill \cr { \le \mathop {\max }\limits_{{\xi ^i} \in \left[ {{\rho _1},{\rho _2}} \right]} \;F\left[ {\sum\limits_{i = 0}^l {{a_i}({f^{\left( m \right)}}\left( {{\rho _1}} \right)g\left( {{\rho _1}} \right)h\left( {{{{\rho _2} - {\xi ^i}} \over {{\rho _2} - {\rho _1}}}} \right)} } \right.} \hfill \cr {\;\;\;\left. {\left. { + \;{f^{\left( m \right)}}\left( {{\rho _2}} \right)g\left( {{\rho _2}} \right)h\left( {{{{\xi ^i} - {\rho _1}} \over {{\rho _2} - {\rho _1}}}} \right)} \right),{f^{\left( m \right)}}\left( {{\xi ^i}} \right)} \right].} \hfill \cr }
In the last part of the section, we will provide a discussion on h-convex functions.
Remark 2
Let f : [ρ1, ρ2] → ℝ be a h-convex function. We apply inequality (1.4) on the concave function h by setting x = ρ1, y = ρ2,
u = \lambda {\rho _1} + \left( {1 - \lambda } \right){\rho _2}
and then proceed to solve for
\lambda = {{u - {\rho _2}} \over {{\rho _1} - {\rho _2}}}.
Substituting this into the inequality (1.4), we obtain
(3.2)
f\left( u \right) \le h\left( {{{{\rho _2} - u} \over {{\rho _2} - {\rho _1}}}} \right)f\left( {{\rho _1}} \right) + h\left( {{{u - {\rho _1}} \over {{\rho _2} - {\rho _1}}}} \right)f\left( {{\rho _2}} \right).
By substituting u = xj , where j ∈ {0, . . . , m}, into (3.2) and multiplying each inequality by λj , j ∈ {0, . . . ,m}, such that
\sum\nolimits_{j = 0}^m {{\lambda _j} = 1}
, we obtain the following result
{\lambda _j}f\left( {{x_j}} \right) \le {\lambda _j}h\left( {{{{\rho _2} - {x_j}} \over {{\rho _2} - {\rho _1}}}} \right)f\left( {{\rho _1}} \right) + {\lambda _j}h\left( {{{{x_j} - {\rho _1}} \over {{\rho _2} - {\rho _1}}}} \right)f\left( {{\rho _2}} \right),\;\;j \in \left\{ {0,\ldots ,m} \right\}.
By summing these inequalities from j = 0 to j = m, we obtain
\sum\limits_{j = 0}^m {{\lambda _j}f\left( {{x_j}} \right)} \le \sum\limits_{j = 0}^m {{\lambda _j}h\left( {{{{\rho _2} - {x_j}} \over {{\rho _2} - {\rho _1}}}} \right)f\left( {{\rho _1}} \right)} + \sum\limits_{j = 0}^m {{\lambda _j}h\left( {{{{x_j} - {\rho _1}} \over {{\rho _2} - {\rho _1}}}} \right)f\left( {{\rho _2}} \right)} .
Since h is a concave function and f is assumed to be a nonnegative, we have, by denoting
\bar x = \sum\nolimits_{j = 0}^m {{\lambda _j}{x_j}}
, the following result:
\matrix{ {\sum\limits_{j = 0}^m {{\lambda _j}f\left( {{x_j}} \right)} } \hfill & { \le \;\sum\limits_{j = 0}^m {{\lambda _j}h\left( {{{{\rho _2} - {x_j}} \over {{\rho _2} - {\rho _1}}}} \right)f\left( {{\rho _1}} \right)} + \sum\limits_{j = 0}^m {{\lambda _j}h\left( {{{{x_j} - {\rho _1}} \over {{\rho _2} - {\rho _1}}}} \right)f\left( {{\rho _2}} \right)} } \hfill \cr {} \hfill & { \le \;\;h\left( {{{{\rho _2} - \bar x} \over {{\rho _2} - {\rho _1}}}} \right)f\left( {{\rho _1}} \right) + h\left( {{{\bar x - {\rho _1}} \over {{\rho _2} - {\rho _1}}}} \right)f\left( {{\rho _2}} \right).} \hfill \cr }
Now we apply the obtained result to the function F of two variables, which is monotonic in its first argument (compare [5] and [6]), and get
\matrix{ {F\left( {\sum\limits_{j = 0}^m {{\lambda _j}f\left( {{x_j}} \right),f\left( {\bar x} \right)} } \right) \le F\left( {h\left( {{{{\rho _2} - \bar x} \over {{\rho _2} - {\rho _1}}}} \right)f\left( {{\rho _1}} \right) + h\left( {{{\bar x - {\rho _1}} \over {{\rho _2} - {\rho _1}}}} \right)f\left( {{\rho _2}} \right),f\left( {\bar x} \right)} \right)} \hfill \cr {\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\; \le \mathop {\max }\limits_{\xi \in \left[ {{\rho _1},{\rho _2}} \right]} {\rm{\;}}F\left( {h\left( {{{{\rho _2} - \xi } \over {{\rho _2} - {\rho _1}}}} \right)f\left( {{\rho _1}} \right) + h\left( {{{\xi - {\rho _1}} \over {{\rho _2} - {\rho _1}}}} \right)f\left( {{\rho _2}} \right),f\left( \xi \right)} \right).} \hfill \cr }
If we for the function F set F(u, v) = u − v, we denote
\Phi \left( \xi \right) = h\left( {{{{\rho _2} - \xi } \over {{\rho _2} - {\rho _1}}}} \right)f\left( {{\rho _1}} \right) + h\left( {{{\xi - {\rho _1}} \over {{\rho _2} - {\rho _1}}}} \right)f\left( {{\rho _2}} \right) - f\left( \xi \right).
Now, we will use this result to determine the conversion of inequality (1.4) i.e. we need to find the constant μ such that
h\left( \lambda \right)f\left( x \right) + h\left( {1 - \lambda } \right)f\left( y \right) \le f\left( {\lambda x + \left( {1 - \lambda } \right)y} \right) + \mu
is valid, where
\mu = \;\;\mathop {\max }\limits_{\xi \in \left[ {{\rho _1},{\rho _2}} \right]} \;\Phi \left( \xi \right).
According to the Bolzano–Weierstrass theorem, determining the maximum value of the function Φ requires evaluating it at key points. The potential candidates for the global maximum are the boundary points ξ = ρ1 and ξ = ρ2, along with any critical points where the derivative satisfies Φ′(ξ) = 0, hence to identify the global maximum, we will compute Φ at each of these points and compare their values:
1◦ Φ(ρ1) = h(1)f(ρ1) + h(0)f(ρ2) − f(ρ1),
2◦ Φ(ρ2) = h(0)f(ρ1) + h(1)f(ρ2) − f(ρ2),
3◦ Φ(ξ0) where Φ′(ξ0) = 0 i.e.
0 = - h'\left( {{{{\rho _2} - {\xi _0}} \over {{\rho _2} - {\rho _1}}}} \right){{f\left( {{\rho _1}} \right)} \over {{\rho _2} - {\rho _1}}} + h'\left( {{{{\xi _0} - {\rho _1}} \over {{\rho _2} - {\rho _1}}}} \right){{f\left( {{\rho _2}} \right)} \over {{\rho _2} - {\rho _1}}} - f'\left( {{\xi _0}} \right).
Now, we need to analyze the second derivative of the function Φ
\Phi ''\left( \xi \right) = \left( {h''\left( {{{{\rho _2} - \xi } \over {{\rho _2} - {\rho _1}}}} \right)f\left( {{\rho _1}} \right) + h''\left( {{{\xi - {\rho _1}} \over {{\rho _2} - {\rho _1}}}} \right)f\left( {{\rho _2}} \right)} \right){1 \over {{{({\rho _2} - {\rho _1})}^2}}} - f''\left( \xi \right),
to determine type of local extrema. For a function f that is h-convex, determining the maximum of the function Φ is not a straightforward task. The complexity arises from the dependence on the (classical) properties of both functions f and h, which influence the analytical behavior of Φ in the given domain. For example, consider the functions f and hk defined as hk(x) = xk, f(x) = xλ, x > 0, k, λ ∈ ℝ. From [9], we know that the function f is hk-convex if:
In the first case, the function f is both hk-convex and convex in the classical sense, while hk is concave in the classical sense. In the second case, the function f is hk-convex and concave in the classical sense, while hk is concave for 0 < k < λ and convex for k < 0 (in the classical sense). We consider here the case (i).
\Phi \left( {{\rho _1}} \right) = h\left( 1 \right)f\left( {{\rho _1}} \right) + h\left( 0 \right)f\left( {{\rho _2}} \right) - f\left( {{\rho _1}} \right) = 1 \cdot f\left( {{\rho _1}} \right) + 0 \cdot f\left( {{\rho _2}} \right) - f\left( {{\rho _1}} \right) = 0,
and
\Phi \left( {{\rho _2}} \right) = h\left( 0 \right)f\left( {{\rho _1}} \right) + h\left( 1 \right)f\left( {{\rho _2}} \right) - f\left( {{\rho _2}} \right) = 0 \cdot f\left( {{\rho _1}} \right) + 1 \cdot f\left( {{\rho _2}} \right) - f\left( {{\rho _2}} \right) = 0.
Also, according to inequality (1.4), we know that the function Φ is non-negative, Φ″ ≤ 0, and we can conclude that Φ achieves a global maximum at some interior point ξ0, where Φ′(ξ0) = 0.