Mean value inequalities #
In this file we prove several inequalities for finite sums, including AM-GM inequality,
HM-GM inequality, Young's inequality, Hölder inequality, and Minkowski inequality. Versions for
integrals of some of these inequalities are available in
Mathlib/MeasureTheory/Integral/MeanInequalities.lean.
Main theorems #
AM-GM inequality: #
The inequality says that the geometric mean of a tuple of non-negative numbers is less than or equal
to their arithmetic mean. We prove the weighted version of this inequality: if
We prove a few versions of this inequality. Each of the following lemmas comes in two versions:
a version for real-valued non-negative functions is in the Real namespace, and a version for
NNReal-valued functions is in the NNReal namespace.
geom_mean_le_arith_mean_weighted: weighted version for functions onFinsets;geom_mean_le_arith_mean2_weighted: weighted version for two numbers;geom_mean_le_arith_mean3_weighted: weighted version for three numbers;geom_mean_le_arith_mean4_weighted: weighted version for four numbers.
HM-GM inequality: #
The inequality says that the harmonic mean of a tuple of positive numbers is less than or equal
to their geometric mean. We prove the weighted version of this inequality: if
The inequalities are proven only for real valued positive functions on Finsets, and namespaced in
Real. The weighted version follows as a corollary of the weighted AM-GM inequality.
Young's inequality #
Young's inequality says that for non-negative numbers a, b, p, q such that
This inequality is a special case of the AM-GM inequality. It is then used to prove Hölder's inequality (see below).
Hölder's inequality #
The inequality says that for two conjugate exponents p and q (i.e., for two positive numbers
such that
We give versions of this result in ℝ, ℝ≥0 and ℝ≥0∞.
There are at least two short proofs of this inequality. In our proof we prenormalize both vectors,
then apply Young's inequality to each
Minkowski's inequality #
The inequality says that for p ≥ 1 the function
We give versions of this result in Real, ℝ≥0 and ℝ≥0∞.
We deduce this inequality from Hölder's inequality. Namely, Hölder inequality implies that b such that
TODO #
- each inequality
A ≤ Bshould come with a theoremA = B ↔ _; one of the ways to prove them is to defineStrictConvexOnfunctions. - generalized mean inequality with any
p ≤ q, including negative numbers; - prove that the power mean tends to the geometric mean as the exponent tends to zero.
AM-GM inequality #
AM-GM inequality: The geometric mean is less than or equal to the arithmetic mean, weighted version for real-valued nonnegative functions.
AM-GM inequality - equality condition: This theorem provides the equality condition for the positive weighted version of the AM-GM inequality for real-valued nonnegative functions.
AM-GM inequality - equality condition: This theorem provides the equality condition for the weighted version of the AM-GM inequality for real-valued nonnegative functions.
AM-GM inequality - strict inequality condition: This theorem provides the strict inequality condition for the positive weighted version of the AM-GM inequality for real-valued nonnegative functions.
HM-GM inequality #
HM-GM inequality: The harmonic mean is less than or equal to the geometric mean, weighted version for real-valued nonnegative functions.
Young's inequality #
Young's inequality, ℝ≥0∞ version with real conjugate exponents.
Hölder's and Minkowski's inequalities #
Hölder inequality: The scalar product of two functions is bounded by the product of their
L^p and L^q norms when p and q are conjugate exponents. Version for sums over finite sets,
with ℝ≥0-valued functions.
Hölder inequality: the scalar product of two functions is bounded by the product of their
L^p and L^q norms when p and q are conjugate exponents. A version for NNReal-valued
functions. For an alternative version, convenient if the infinite sums are already expressed as
p-th powers, see inner_le_Lp_mul_Lq_hasSum.
Hölder inequality: the scalar product of two functions is bounded by the product of their
L^p and L^q norms when p and q are conjugate exponents. A version for NNReal-valued
functions. For an alternative version, convenient if the infinite sums are not already expressed as
p-th powers, see inner_le_Lp_mul_Lq_tsum.
The L_p seminorm of a vector f is the greatest value of the inner product
∑ i ∈ s, f i * g i over functions g of L_q seminorm less than or equal to one.
Minkowski inequality: the L_p seminorm of the infinite sum of two vectors is less than or
equal to the infinite sum of the L_p-seminorms of the summands, if these infinite sums both
exist. A version for NNReal-valued functions. For an alternative version, convenient if the
infinite sums are already expressed as p-th powers, see Lp_add_le_hasSum_of_nonneg.
Minkowski inequality: the L_p seminorm of the infinite sum of two vectors is less than or
equal to the infinite sum of the L_p-seminorms of the summands, if these infinite sums both
exist. A version for NNReal-valued functions. For an alternative version, convenient if the
infinite sums are not already expressed as p-th powers, see Lp_add_le_tsum_of_nonneg.
Hölder inequality: the scalar product of two functions is bounded by the product of their
L^p and L^q norms when p and q are conjugate exponents. Version for sums over finite sets,
with real-valued functions.
Hölder inequality: the scalar product of two functions is bounded by the product of their
L^p and L^q norms when p and q are conjugate exponents. Version for sums over finite sets,
with real-valued nonnegative functions.
Weighted Hölder inequality in terms of Finset.expect.
Hölder inequality: the scalar product of two functions is bounded by the product of their
L^p and L^q norms when p and q are conjugate exponents. A version for ℝ-valued functions.
For an alternative version, convenient if the infinite sums are already expressed as p-th powers,
see inner_le_Lp_mul_Lq_hasSum_of_nonneg.
Hölder inequality: the scalar product of two functions is bounded by the product of their
L^p and L^q norms when p and q are conjugate exponents. A version for NNReal-valued
functions. For an alternative version, convenient if the infinite sums are not already expressed as
p-th powers, see inner_le_Lp_mul_Lq_tsum_of_nonneg.
For 1 ≤ p, the p-th power of the sum of f i is bounded above by a constant times the
sum of the p-th powers of f i. Version for sums over finite sets, with nonnegative ℝ-valued
functions.
Minkowski inequality: the L_p seminorm of the sum of two vectors is less than or equal
to the sum of the L_p-seminorms of the summands. A version for ℝ-valued nonnegative
functions.
Minkowski inequality: the L_p seminorm of the infinite sum of two vectors is less than or
equal to the infinite sum of the L_p-seminorms of the summands, if these infinite sums both
exist. A version for ℝ-valued functions. For an alternative version, convenient if the infinite
sums are already expressed as p-th powers, see Lp_add_le_hasSum_of_nonneg.
Minkowski inequality: the L_p seminorm of the infinite sum of two vectors is less than or
equal to the infinite sum of the L_p-seminorms of the summands, if these infinite sums both
exist. A version for ℝ-valued functions. For an alternative version, convenient if the infinite
sums are not already expressed as p-th powers, see Lp_add_le_tsum_of_nonneg.
Hölder inequality: the scalar product of two functions is bounded by the product of their
L^p and L^q norms when p and q are conjugate exponents. Version for sums over finite sets,
with ℝ≥0∞-valued functions.