Useful inequalities cheat sheet

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Useful Inequalities
Cauchy-Schwarz
Minkowski
Hölder
n
P
n
P
i=1
n
P
i=1
Bernoulli
xi y i
i=1
2
≤
|xi + yi |p
|xi yi | ≤
n
P
i=1
1
p
n
P
i=1
x2i
n
P
≤
|xi |p
(1 + x)r ≥ 1 + rx
{x2 > 0}
i=1
i=1
1/p (1 +
x)r
(1 +
nx)n+1
≤
≤1+
1
p
n
P
i=1
+
|yi |q
n
P
i=1
|yi |p
1/q
1
p
for p ≥ 1.
for p, q > 1,
1
p
1
q
+
≥ (1 + (n +
for x ∈ [−1,
1
),
r−1
for x ∈ R, n ∈ N.
ex
≥
xe
1
2−x
1+
xn
n!
for x ∈ R, and
ex ≥ 1 + x +
e−x
≥ 1 + x,
≤1−
x2
2
x
2
− 1) ≤
x2
n
≥ ex 1 −
+1≤
ex
≤ 1+
for x ≥ 0, reverse for x ≤ 0.
for x ∈ [0, ∼ 1.59] and
< xx < x2 − x + 1
x1/r (x
x n
n
rx(x1/r
2−x
for n > 1, |x| ≤ n.
x n+x/2
n
≤1−
x
2
for x ∈ [0, 1].
− 1)
for x, y > 0.
2 − y − e−x−y ≤ 1 + x ≤ y + ex−y , and ex ≤ x + ex
logarithm
x−1
x
2x
2+x
≤ ln(x) ≤
x2 −1
2x
1
n
ln(n + 1) < ln(n) +
ln(1 + x) ≥
x
2
ln(1 + x) ≥ x −
x3
2
x−
hyperbolic
x cos x ≤
2
x
π
−
2
for x, y ∈ R.
1
i=1 i
x3
2
Stirling
e
means
min xi ≤
Lehmer
n n
e
≤ 2n .
P
P
√
Heinz
√
Newton
≤
√
2πn
n
P −1
xi
√1
x
<
√
x+1−
n n 1/(12n+1)
e
e
≤(
Q
xi )1/n ≤
√
√
√
x−1 < 2 x−2 x−1
≤ n! ≤
1
n
P
√
n n 1/12n
e
e
2πn
q
xi ≤
1
n
P
for x ≥ 1.
≤ en
P 2
x
P i
xi
xi 2 ≤
n n
e
≤ max xi
P
P
p 1/p , w ≥ 0,
Mp ≤ Mq for p ≤ q, where Mp =
i
i wi |xi |
i wi = 1.
Q
In the limit M0 = i |xi |wi , M−∞ = mini {xi }, M∞ = maxi {xi }.
log mean
i
i
wi |xi |p
wi |xi |p−1
xy ≤
xy ≤
√
x+
2
P
wi |xi |q
≤ P i
q−1
i wi |xi |
√
y
1
(xy) 4 ≤
x1−α y α +xα y 1−α
2
Sk 2 ≥ Sk−1 Sk+1
P
1
Sk = n
k
Jensen
≤
1≤i1 <···<ik ≤n
x−y
ln(x)−ln(y)
x+y
2
p
k
and
for p ≤ q, wi ≥ 0.
≤
√
x+
2
√
y
2
≤
x+y
2
for x, y > 0.
for x, y > 0, α ∈ [0, 1].
Sk ≥
p
(k+1)
ai 1 ai 2 · · · aik ,
Sk+1
and
for 1 ≤ k < n,
ai ≥ 0.
≤ ln(n) + 1
Chebyshev
x
√
3
x3
3
≤ tan x
≤ sin x,
x2
,
sinh x
all for x ∈ 0,
for x ∈ R, α ∈ [−1, 1].
i
P
pi xi ≤ i pi ϕ (xi )
n
P
f (ai )g(bi )pi ≥
n
P
ai b i ≥
i=1
≤ x cos2 (x/2) ≤ sin x ≤ (x cos x + 2x)/3 ≤
≤ sin x ≤ x cos(x/2) ≤ x ≤ x +
P
where pi ≥ 0,
P
pi = 1, and ϕ convex.
π
2
.
n
P
f (ai )pi
i=1
n
P
i=1
n
P
g(bi )pi ≥
f (ai )g(bn−i+1 )pi
i=1
for a1 ≤ · · · ≤ an , b1 ≤ · · · ≤ bn and f, g nondecreasing, pi ≥ 0,
Alternatively: E f (X)g(X) ≥ E f (X) E g(X) .
for x ∈ [0, ∼ 0.43], reverse elsewhere.
√
≤ x 3 cos x ≤ x − x3 /6 ≤ x cos
ϕ
Alternatively: ϕ (E [X]) ≤ E [ϕ(X)]. For concave ϕ the reverse holds.
for x ∈ [0, ∼ 0.45], reverse elsewhere.
ex(α+x/2)
nn
kk (n−k)n−k
1
ln(x) ≤ n(x n − 1) for x, n > 0.
Pn
x cos x
1−x2 /3
cosh(x) + α sinh(x) ≤
√
√
2 x+1−2 x <
for x ≥ 0, reverse for x ∈ (−1, 0].
x3
4
+
x2
2
≤ x cos x ≤
x3
sinh2 x
≤
square root
Maclaurin-
for x ∈ [0, ∼ 2.51], reverse elsewhere.
x2
2
ln(1 − x) ≥ −x −
trigonometric
≤ x − 1,
√x
x+1
≤ ln(1 + x) ≤
for x, n > 0.
for x ∈ (0, 1).
for x, r ≥ 1.
xy
y
xy + y x > 1 and ex > 1 + x
> e x+y
y
≤
Pd
n
n
for n ≥ d ≥ 0.
≤ nd + 1 and
i=0 i ≤ 2
Pd
d
n
en
for n ≥ d ≥ 1.
i=0 i ≤
d
Pd
n
n
d
for n
≥ d ≥ 0.
i=0 i ≤ d 1 + n−2d+1
2
P
n
n
αn n
1−α
≤ i=0 i ≤ 1−2α αn for α ∈ (0, 21 ).
αn
power means
(iv) q < 0 < p , −q > x > 0, (v) q < 0 < p , −p < x < 0.
x n
n
n
k
and
n
i=0 i
r > 1.
(ii) − p < −q < x < 0, (iii) − q > −p > x > 0. Reverse for:
ex ≥ 1 +
(en)k
kk
≤
Pd
= 1.
(a + b)n ≤ an + nb(a + b)n−1 for a, b ≥ 0, n ∈ N.
p
q
≥ 1 + xq
for (i) x > 0, p > q > 0,
1+ x
p
exponential
nk
k!
≤
2πnα(1−α)
for x ∈ [−1, 0], n ∈ N.
1)x)n
n
k
≤
n
n
√
1
1
for n ≥ k ≥ 0 and √4πn (1 − 8n
≤ √4πn (1 − 9n
≤ n
) ≤ 2n
).
k
n
n1 +n2
n1 n2
for n1 ≥ k1 ≥ 0, n2 ≥ k2 ≥ 0.
≤ k +k
k1 k2
1
2
√
nH(α)
π
n , H(x) = − log2 (xx (1−x)1−x ).
G ≤ αn ≤ G for G = √ 2
2
for x ∈ [0, 1], r ∈ R \ (0, 1).
rx
1−(r−1)x
(n−k+1)k
}
k!
k
max { n
,
kk
nk
4k!
for x ≥ −1, r ∈ R \ (0, 1). Reverse for r ∈ [0, 1].
1
1−nx
(1 +
yi2
|xi |p
(1 + x)r ≤ 1 + (2r − 1)x
x)n
n
P
binomial
v0.28 · January 13, 2016
rearrangement
i=1
n
P
i=1
ai bπ(i) ≥
n
P
ai bn−i+1
i=1
for a1 ≤ · · · ≤ an ,
b1 ≤ · · · ≤ bn and π a permutation of [n]. More generally:
n
n
n
P
P
P
fi (bi ) ≥
fi (bπ(i) ) ≥
fi (bn−i+1 )
i=1
i=1
i=1
with fi+1 (x) − fi (x) nondecreasing for all 1 ≤ i < n.
P
pi = 1.
Weierstrass
Q
1 − xi
i
wi
P
≥1−
i
w i xi
where xi ≤ 1, and
Carleman
either wi ≥ 1 (for all i) or wi ≤ 0 (for all i).
P
If wi ∈ [0, 1],
wi ≤ 1 and xi ≤ 1, the reverse holds.
Young
Kantorovich
( px1p
+
P
xi 2
i
1 −1
)
qxq
xi
yi
0<m≤
sum-integral
RU
Cauchy
ϕ′ (a) ≤
L−1
Hermite
ϕ
Chong
n
P
Gibbs
P
Shapiro
i
i
ai
ai log
ai ϕ
ai
bi
≤ ϕ′ (b)
Rb
a
ai
n
Q
i=1
a
b
b
a
≤aϕ
≥
L
+
1
q
= 1.
for f nondecreasing.
ϕ(a)+ϕ(b)
2
n
Q
ai a i ≥
ai aπ(i)
Karamata
Muirhead
for ai > 0.
Hadamard
(det A)2
Schur
Pn
i=1
≤
A2ij
i=1 j=1
λ2i ≤
Pn
Hilbert
i,j=1
A2ij
and
i=1
di ≤
Pk
i=1
λi
Aczél
Qn
Pn
a
P
x i
a i xi
Qn i=1 i a ≤ Pn i=1
for xi ∈ [0, 12 ], ai ∈ [0, 1],
ai = 1.
i
i=1 (1 − xi )
i=1 ai (1 − xi )
a1 b 1 −
given that
Mahler
n
Q
Pn
a21
xi + yi
i=1
Abel
Milne
b1 min
k
Pn
ai b i
Pn
i=2
k
P
i=1
i=1 (ai
>
+ bi )
≥ a21 −
2
i=2 ai
1/n
ai ≤
2
≥
n
P
i=1
n
Q
i=1
or
1/n
xi
b21
+
Pn
>
2 b2
1
i=2 ai
Pn
2
i=2 bi .
n
Q
i=1
ai bi ≤ b1 max
P
k
ai bi
n
i=1 ai +bi
1/n
yi
−
Pn
2
i=2 bi
i=1
ϕ(ai ) ≥
1
n!
P
π
P∞
m=1
≤
Pn
i=1
Pn
i=1 bi
m P
n
Q
j=1 i=1
aiπ(j)
for a1 ≥ a2 ≥ · · · ≥ an and b1 ≥ · · · ≥ bn ,
Pt
Pt
i=1 ai ≥
i=1 bi for all 1 ≤ t ≤ n,
1
n!
P
n
1
· · · xbπ(n)
xbπ(1)
π
1
c2 +1/2
<
Copson
∞ P
P
n=1
n=1
Kraft
LYM
P
k≥n
4
≤ π2
m=1
P
P∞
n=1
a2n
P∞
2n
n=1 (n2 +c2 )2
ak
k
p
2−c(i) ≤ 1
X∈A
Sauer-Shelah
P∞
≤π
a2m
1 P∞
2
n −1
|X|
≤ pp
∞
P
P∞
n=1
1
c2
<
an p
n=1
n2 a2n
1
2 2
n=1 bn
for am , bn ∈ R.
for an ≥ 0, p > 1, reverse if p ∈ (0, 1).
for c(i) depth of leaf i of binary tree, sum over all leaves.
≤ 1, A ⊂ 2[n] , no set in A is subset of another set in A.
|A| ≤ |str(A)| ≤
Pr
Pr
vc(A)
P
i=0
n
i
for A ⊆ 2[n] , and
vc(A) = max{|X| : X ∈ str(A)}.
n
W
k
P
Ai ≤
(−1)j−1 Sj
for 1 ≤ k ≤ n, k odd,
n
W
k
P
Ai ≥
(−1)j−1 Sj
for 2 ≤ k ≤ n, k even.
i=1
Sk =
for an ∈ R.
for c 6= 0.
str(A) = {X ⊆ [n] : X shattered by A},
for b1 ≥ · · · ≥ bn ≥ 0.
ai
am bn
n=1 m+n
Mathieu
Bonferroni
ai
j=1 i=1
aij ≤
With max{m, n} instead of m + n, we have 4 instead of π.
P∞ a1 +a2 +···+an p p p P∞
p
≤ p−1
for an ≥ 0, p > 1.
n=1 an
n=1
n
where xi , yi > 0.
i=1
m P
n
Q
and
ϕ(bi )
i=1
P∞
i=1
k
P
Pn
n
1
≥
· · · xa
xa
π(n)
π(1)
an
λ1 ≥ · · · ≥ λn the eigenvalues, d1 ≥ · · · ≥ dn the diagonal elements.
Ky Fan
Pn
P∞
for 1 ≤ k ≤ n.
A is an n × n matrix. For the second inequality A is symmetric.
aiπ(j)
j=1 i=1
|ak |
k=1
where 0 ≤ ai1 ≤ · · · ≤ aim for i = 1, . . . , n and π is a permutation of [n].
Q n
Pn
Qn
for |ai |, |bi | ≤ 1.
i=1 ai −
i=1 bi ≤
i=1 |ai − bi |
Qn
Q
n
n
i=1 (α + ai ) ≥ (1 + α) , where
i=1 ai ≥ 1, ai > 0, α > 0.
P
P
P
P
1+y 1−y
1−y 1+y
1+x 1−x
1−x 1+x
bi
bi
≥
bi
bi
i ai
i ai
i ai
i ai
Carlson
where A is an n × n matrix.
Pk
Pn
xi ≥ 0 and the sums extend over all permutations π of [n].
where xi > 0, (xn+1 , xn+2 ) := (x1 , x2 ),
where x, y, z ≥ 0, t > 0
n
m Q
P
≤e
where a1 ≥ a2 ≥ · · · ≥ an and b1 ≥ b2 ≥ · · · ≥ bn and {ak } {bk },
i=1
xt (x − y)(x − z) + y t (y − z)(y − x) + z t (z − x)(z − y) ≥ 0
1/k
with equality for t = n and ϕ is convex (for concave ϕ the reverse holds).
Hardy
n P
n
Q
aij ≥
|ai |
and {ai } {bi } (majorization), i.e.
and n ≤ 12 if even, n ≤ 23 if odd.
Schur
m Q
n
P
k
i=1
for 1 ≥ x ≥ y ≥ 0, and i = 1, . . . , n.
for ϕ convex.
for ai , bi ≥ 0, or more generally:
P
P
for ϕ concave, and a =
ai , b =
bi .
n
2
Callebaut
f (x) dx
k=1
j=1 i=1
where a < b, and ϕ convex.
ϕ(x) dx ≤
≥ a log
bi for x, y ≥ 0, p, q > 0,
R U +1
f (i) ≤
and
xi
xi+1 +xi+2
i=1
i=L
≥n
aπ(i)
n
P
PU
1
b−a
≤
+
1
p
for xi , yi > 0,
√
A = (m + M )/2, G = mM .
≤ M < ∞,
f (b)−f (a)
b−a
yq
q
2
A 2 P
yi 2 ≤ G
i xi yi
i
f (x) dx ≤
a+b
2
i=1
P
P
≤ xy ≤
xp
p
sum & product
Q
Pn
j=1
j=1
P
1≤i1 <···<ik ≤n
Pr Ai1 ∧ · · · ∧ Aik
where Ai are events.
Bhatia-Davis
Samuelson
Markov
Chebyshev
2nd moment
kth moment
Chernoff
Var[X] ≤ M − E[X] E[X] − m
where X ∈ [m, M ].
Paley-Zygmund
√
√
µ − σ n − 1 ≤ xi ≤ µ + σ n − 1 for i = 1, . . . , n.
P
P
Where µ =
xi /n , σ 2 =
(xi − µ)2 /n.
Vysochanskij-
Pr X − E[X] ≥ t ≤ Var[X]/t2 where t > 0.
Pr X − E[X] ≥ t ≤ Var[X]/(Var[X] + t2 ) where t > 0.
Pr X > 0 ≥ (E[X])2 /(E[X 2 ]) where E[X] ≥ 0.
Pr X = 0 ≤ Var[X]/(E[X 2 ]) where E[X 2 ] 6= 0.
Pr X ≥ t ≤
n
k
pk
t
k
for Xi ∈ {0, 1} k-wise i.r.v., E[Xi ] = p, X =
P
Hoeffding
Kolmogorov
−2δ 2
Pn
2
i=1 (bi − ai )
P
Xi ∈ [ai , bi ] (w. prob. 1), X =
Xi , δ ≥ 0.
Bennett
Bernstein
Efron-Stein
Xi .
McDiarmid
Janson
for Xi i.r.v.,
A related lemma, assuming E[X] = 0, X ∈ [a, b] (w. prob. 1) and λ ∈ R:
2
λ (b − a)2
E eλX ≤ exp
8
P
Pr max |Sk | ≥ ε ≤ ε12 Var[Sn ] = ε12
Var[Xi ]
σ 2 = Var[X] < ∞, τ 2 = Var[X] + (E[X] − m)2 = E[(X − m)2 ].
Pr max |Sk | ≥ 3α ≤ 3 max Pr |Sk | ≥ α
1≤k≤n
1≤k≤n
P
where Xi are i.r.v., Sk = ki=1 Xi , α ≥ 0.
Lovász
for martingale (Xk ) and ε > 0.
nσ 2
Mε ≤ exp − 2 θ
where Xi i.r.v.,
2
M
nσ
i=1
P
1
Var[Xi ], |Xi | ≤ M (w. prob. 1), ε ≥ 0,
E[Xi ] = 0, σ 2 = n
Pr
n
P
Xi ≥ ε
θ(u) = (1 + u) log(1 + u) − u.
n
P
−ε2
Pr
Xi ≥ ε ≤ exp
2
2(nσ + M ε/3)
i=1
for Xi i.r.v.,
1 P
E[Xi ] = 0, |Xi | < M (w. prob. 1) for all i, σ 2 = n
Var[Xi ], ε ≥ 0.
−δ 2
P
Pr Xn − X0 ≥ δ ≤ 2 exp
for martingale (Xk ) s.t.
2
2 n
i=1 ci
Xi − Xi−1 < ci (w. prob. 1), for i = 1, . . . , n, δ ≥ 0.
Var[Z] ≤
1
2
E
n
P
i=1
Z − Z (i)
2
for Xi , Xi ′ ∈ X i.r.v.,
f : X n → R, Z = f (X1 , . . . , Xn ), Z (i) = f (X1 , . . . , Xi ′ , . . . , Xn ).
−2δ 2
Pr Z − E[Z] ≥ δ ≤ 2 exp Pn
for Xi , Xi ′ ∈ X i.r.v.,
2
i=1 ci
Z, Z (i) as before, s.t. Z − Z (i) ≤ ci for all i, and δ ≥ 0.
V B i ≤ M exp
M ≤ Pr
Q
M = (1 − Pr[Bi ]), ∆ =
∆
2 − 2ε
P
i6=j,Bi ∼Bj
Pr
V
Q
B i ≥ (1 − xi ) > 0
where Pr[Bi ] ≤ ε for all i,
Pr[Bi ∧ Bj ].
where Pr[Bi ] ≤ xi ·
Q
(i,j)∈D
(1 − xj ),
for xi ∈ [0, 1) for all i = 1, . . . , n and D the dependency graph.
If each Bi mutually indep. of the set of all other events, exc. at most d,
V B i > 0.
Pr[Bi ] ≤ p for all i = 1, . . . , n , then if ep(d + 1) ≤ 1 then Pr
i
where X1 , . . . , Xn are i.r.v., E[Xi ] = 0,
P
Var[Xi ] < ∞ for all i, Sk = ki=1 Xi and ε > 0.
3
Pr max1≤k≤n |Xk | ≥ ε ≤ E |Xn | /ε
n k̂ (1+δ)µ
p /
k̂
k̂
Pr X − E[X] ≥ δ ≤ 2 exp
k
3τ
Azuma
for Xi ∈ [0, 1] k-wise i.r.v.,
P
µ
, δ > 0.
k ≥ k̂ = ⌈µδ/(1 − p)⌉, E[Xi ] = pi , X =
Xi , µ = E[X], p = n
Pr X ≥ (1 + δ)µ ≤
q
8
if λ ≥
Pr X − E[X] ≥ λσ ≤ 9λ4 2
,
3
2
2τ
4τ
if ε ≥ √ ,
Pr X − m ≥ ε ≤ 9ε2
3
ε
2τ
Pr X − m ≥ ε ≤ 1 − √
if ε ≤ √
.
Where X is a unimodal r.v. with mode m,
Doob
Pr[X ≥ t] ≤ F (a)/at for X r.v., Pr[X = k] = pk ,
P
F (z) = k pk z k probability gen. func., and a ≥ 1.
µ
eδ
−µδ 2
Pr X ≥ (1 + δ)µ ≤
≤
exp
3
(1 + δ)(1+δ)
P
for Xi i.r.v. from [0,1], X =
Xi , µ = E[X], δ ≥ 0 resp. δ ∈ [0, 1).
µ
e−δ
−µδ 2
Pr X ≤ (1 − δ)µ ≤
for δ ∈ [0, 1).
≤
exp
2
(1 − δ)(1−δ)
Further from the mean: Pr X ≥ R ≤ 2−R for R ≥ 2eµ (≈ 5.44µ).
Petunin-Gauss
Etemadi
E (X − µ)k
and
Pr X − µ ≥ t ≤
tk
k/2
nk
for Xi ∈ [0, 1] k-wise indep. r.v.,
Pr X − µ ≥ t ≤ Ck
t2
√
P
X=
Xi , i = 1, . . . , n, µ = E[X], Ck = 2 πke1/6k ≤ 1.0004, k even.
for X ≥ 0,
Var[X] < ∞, and µ ∈ (0, 1).
Pr |X| ≥ a ≤ E |X| /a where X is a random variable (r.v.), a > 0.
Pr X ≤ c ≤ (1 − E[X])/(1 − c) for X ∈ [0, 1] and c ∈ 0, E[X] .
Pr X ∈ S] ≤ E[f (X)]/s for f ≥ 0, and f (x) ≥ s > 0 for all x ∈ S.
Var[X]
(1 − µ)2 (E[X])2 + Var[X]
Pr X ≥ µ E[X] ≥ 1 −
✁✂
László Kozma · latest version: http://www.Lkozma.net/inequalities_cheat_sheet
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