El cálculu multivariable (o cálculu en delles variables) nun ye más que la estensión del cálculu infinitesimal a funciones angulares y vectoriales de delles variables.
Campo angular con dos variables
Cálculu diferencial en campos angulares y vectoriales
Funciones de Rn en Rm . Campos angulares y vectoriales
Vamos Formular les definiciones pa campos vectoriales. Tamién van ser válides para campos angulares. Sía :
f
:
V
⟶
W
{\displaystyle \mathbf {f} :V\longrightarrow W}
un campu vectorial que fai corresponder a tou puntu P definíu biunívocamente pol so vector posición un vector
f
(
O
P
)
{\displaystyle \mathbf {f} {\big (}\mathbf {OP} {\big )}
onde'l puntu O ye'l nuesu orixe de coordenaes .
V
⊆
R
n
,
W
⊆
R
m
,
{\displaystyle V\subseteq \mathbb {R} ^{n},W\subseteq \mathbb {R} ^{m},}
con
n
>
1
{\displaystyle n>1}
y
m
⩾
1
{\displaystyle m\geqslant 1}
. Cuando
m
=
1
{\displaystyle m=1}
tenemos un campu angular . Pa
m
>
1
{\displaystyle m>1}
tenemos un campu vectorial . Vamos Utilizar la norma euclídea pa topar la magnitú de los vectores .
Llendes y continuidá
Sean
a
∈
R
n
{\displaystyle \mathbf {a} \in \mathbb {R} ^{n}
y
b
∈
R
m
.
{\displaystyle \mathbf {b} \in \mathbb {R} ^{m}.}
Escribimos:
lim
x
→
a
f
(
x
)
=
b
{\displaystyle \lim _{\mathbf {x} \to \mathbf {a} }\mathbf {f} {\big (}\mathbf {x} {\big )}=\mathbf {b} }
, :
o bien, :
f
(
x
)
→
b
{\displaystyle \mathbf {f} (\mathbf {x} )\rightarrow \mathbf {b} }
cuando
x
→
a
{\displaystyle \mathbf {x} \rightarrow \mathbf {a} }
pa espresar lo siguiente:
lim
‖
x
−
a
‖
→
0
‖
f
(
x
)
−
b
‖
=
0
{\displaystyle \lim _{\big \|}\mathbf {x-a} {\big \|}\to 0}{\big \|}\mathbf {f} {\big (}\mathbf {x} {\big )}-\mathbf {b} {\big \|}=0}
onde
‖
x
‖
{\displaystyle {\big \|}\mathbf {x} {\big \|}
ye la norma euclídea de
x
{\displaystyle \mathbf {x} }
.
Espresándolo en función de les componentes de
x
=
(
x
1
,
…
,
x
n
)
,
a
=
(
a
1
,
…
,
a
n
)
,
{\displaystyle \mathbf {x} ={\big (}x_{1},\ldots ,x_{n}{\big )},\mathbf {a} ={\big (}a_{1},\ldots ,a_{n}{\big )},}
lim
(
x
1
,
…
,
x
n
)
→
(
a
1
,
…
,
a
n
)
f
(
x
1
,
…
,
x
n
)
=
b
{\displaystyle \lim _{\big (}x_{1},\ldots ,x_{n}{\big )}\to {\big (}a_{1},\ldots ,a_{n}{\big )}\mathbf {f} {\big (}x_{1},\ldots ,x_{n}{\big )}=\mathbf {b} }
o, de forma equivalente, :
lim
x
→
a
f
(
x
)
=
b
{\displaystyle \lim _{\mathbf {x} \to \mathbf {a} }\mathbf {f} {\big (}\mathbf {x} {\big )}=\mathbf {b} }
Dicimos qu'una función
f
{\displaystyle \mathbf {f} }
ye continua en
a
⇔
lim
x
→
a
f
(
x
)
=
f
(
a
)
{\displaystyle \mathbf {a} \Leftrightarrow \lim _{\mathbf {x} \to \mathbf {a} }\mathbf {f} {\big (}\mathbf {x} {\big )}=\mathbf {f} {\big (}\mathbf {a} {\big )}
Demostración
Sabemos qu'a) y b) nel teorema verifíquense si
f
{\displaystyle f}
y
g
{\displaystyle g}
son funciones angulares. Por tanto, si :
b
=
(
b
1
,
…
,
b
m
)
,
c
=
(
c
1
,
…
,
c
m
)
{\displaystyle \mathbf {b} ={\big (}b_{1},\ldots ,b_{m}{\big )},\mathbf {c} ={\big (}c_{1},\ldots ,c_{m}{\big )}
tenemos
a
)
f
(
x
)
=
[
f
1
(
x
)
,
…
,
f
m
(
x
)
]
,
g
(
x
)
=
[
g
1
(
x
)
,
…
,
g
m
(
x
)
]
lim
x
→
a
(
f
+
g
)
(
x
)
=
lim
x
→
a
[
(
f
1
+
g
1
)
(
x
)
,
…
,
(
f
m
+
g
m
)
(
x
)
]
=
[
lim
x
→
a
(
f
1
+
g
1
)
(
x
)
,
…
,
lim
x
→
a
(
f
m
+
g
m
)
(
x
)
]
=
[
lim
x
→
a
f
1
(
x
)
+
lim
x
→
a
g
1
(
x
)
,
…
,
lim
x
→
a
f
m
(
x
)
+
lim
x
→
a
g
m
(
x
)
]
=
(
b
1
+
c
1
,
…
,
b
m
+
c
m
)
=
(
b
1
,
…
,
b
m
)
+
(
c
1
,
…
,
c
m
)
=
b
+
c
{\displaystyle {\begin{array}{rl}a)&\mathbf {f} {\big (}\mathbf {x} )={\big [}f_{1}{\big (}\mathbf {x} {\big )},\ldots ,f_{m}{\big (}\mathbf {x} {\big )}{\big ]},\mathbf {g} {\big (}\mathbf {x} )={\Big [}g_{1}{\big (}\mathbf {x} {\big )},\ldots ,g_{m}{\big (}\mathbf {x} {\big )}{\Big ]}\\&\lim _{\mathbf {x} \to \mathbf {a} }{\big (}\mathbf {f} +\mathbf {g} {\big )}{\big (}\mathbf {x} {\big )}=\lim _{\mathbf {x} \to \mathbf {a} }{\Big [}{\big (}f_{1}+g_{1}{\big )}{\big (}\mathbf {x} {\big )},\ldots ,{\big (}f_{m}+g_{m}{\big )}{\big (}\mathbf {x} {\big )}{\Big ]}=\\&{\Big [}\lim _{\mathbf {x} \to \mathbf {a} }{\big (}f_{1}+g_{1}{\big )}{\big (}\mathbf {x} {\big )},\ldots ,\lim _{\mathbf {x} \to \mathbf {a} }{\big (}f_{m}+g_{m}{\big )}{\big (}\mathbf {x} {\big )}{\Big ]}=\\&{\Big [}\lim _{\mathbf {x} \to \mathbf {a} }f_{1}{\big (}\mathbf {x} {\big )}+\lim _{\mathbf {x} \to \mathbf {a} }g_{1}(\mathbf {x} {\big )},\ldots ,\lim _{\mathbf {x} \to \mathbf {a} }f_{m}{\big (}\mathbf {x} {\big )}+\lim _{\mathbf {x} \to \mathbf {a} }g_{m}{\big (}\mathbf {x} {\big )}{\Big ]}=\\&{\big (}b_{1}+c_{1},\ldots ,b_{m}+c_{m}{\big )}={\big (}b_{1},\ldots ,b_{m}{\big )}+{\big (}c_{1},\ldots ,c_{m}{\big )}=\mathbf {b} +\mathbf {c} \end{array}
b
)
lim
x
→
a
λ
f
(
x
)
=
lim
x
→
a
λ
[
f
1
(
x
)
,
…
,
f
m
(
x
)
]
=
lim
x
→
a
[
λ
f
1
(
x
)
,
…
,
λ
f
m
(
x
)
]
=
[
lim
x
→
a
λ
f
1
(
x
)
,
…
,
lim
x
→
a
λ
f
m
(
x
)
]
=
[
λ
lim
x
→
a
f
1
(
x
)
,
…
,
λ
lim
x
→
a
f
m
(
x
)
]
=
λ
[
lim
x
→
a
f
1
(
x
)
,
…
,
lim
x
→
a
f
m
(
x
)
]
=
λ
(
b
1
,
…
,
b
m
)
=
λ
b
{\displaystyle {\begin{array}{rl}b)&\lim _{\mathbf {x} \to \mathbf {a} }\lambda \mathbf {f} {\big (}\mathbf {x} {\big )}=\lim _{\mathbf {x} \to \mathbf {a} }\lambda {\Big [}f_{1}{\big (}\mathbf {x} {\big )},\ldots ,f_{m}{\big (}\mathbf {x} {\big )}{\Big ]}=\lim _{\mathbf {x} \to \mathbf {a} }{\Big [}\lambda f_{1}{\big (}\mathbf {x} {\big )},\ldots ,\lambda f_{m}{\big (}\mathbf {x} {\big )}{\Big ]}=\\&{\Big [}\lim _{\mathbf {x} \to \mathbf {a} }\lambda f_{1}{\big (}\mathbf {x} {\big )},\ldots ,\lim _{\mathbf {x} \to \mathbf {a} }\lambda f_{m}{\big (}\mathbf {x} {\big )}{\Big ]}={\Big [}\lambda \lim _{\mathbf {x} \to \mathbf {a} }f_{1}{\big (}\mathbf {x} {\big )},\ldots ,\lambda \lim _{\mathbf {x} \to \mathbf {a} }f_{m}{\big (}\mathbf {x} {\big )}{\Big ]}=\\&\lambda {\Big [}\lim _{\mathbf {x} \to \mathbf {a} }f_{1}{\big (}\mathbf {x} {\big )},\ldots ,\lim _{\mathbf {x} \to \mathbf {a} }f_{m}{\big (}\mathbf {x} {\big )}{\Big ]}=\lambda {\big (}b_{1},\ldots ,b_{m}{\big )}=\lambda \mathbf {b} \end{array}
c
)
(
f
⋅
g
)
(
x
)
−
b
⋅
c
=
[
f
(
x
)
−
b
]
⋅
[
g
(
x
)
−
c
]
+
b
⋅
[
g
(
x
)
−
c
]
+
c
⋅
[
f
(
x
)
−
b
]
{\displaystyle c)\quad {\big (}\mathbf {f} \cdot \mathbf {g} {\big )}{\big (}\mathbf {x} {\big )}-\mathbf {b} \cdot \mathbf {c} ={\Big [}\mathbf {f} {\big (}\mathbf {x} {\big )}-\mathbf {b} {\Big ]}\cdot {\Big [}\mathbf {g} {\big (}\mathbf {x} {\big )}-\mathbf {c} {\Big ]}+\mathbf {b} \cdot {\Big [}\mathbf {g} {\big (}\mathbf {x} {\big )}-\mathbf {c} {\Big ]}+\mathbf {c} \cdot {\Big [}\mathbf {f} {\big (}\mathbf {x} {\big )}-\mathbf {b} {\Big ]}
Aplicando la desigualdá triangular y la desigualdá de Cauchy-Schwarz tenemos
|
(
f
⋅
g
)
(
x
)
−
b
⋅
c
|
⩽
‖
f
(
x
)
−
b
‖
⋅
‖
g
(
x
)
−
c
‖
+
‖
b
‖
⋅
‖
g
(
x
)
−
c
‖
+
‖
c
‖
⋅
‖
f
(
x
)
−
b
‖
⇒
0
⩽
lim
‖
x
−
a
‖
→
0
|
(
f
⋅
g
)
(
x
)
−
b
⋅
c
|
⩽
lim
‖
x
−
a
‖
→
0
‖
f
(
x
)
−
b
‖
⋅
lim
‖
x
−
a
‖
→
0
‖
g
(
x
)
−
c
‖
+
‖
b
‖
⋅
lim
‖
x
−
a
‖
→
0
‖
g
(
x
)
−
c
‖
+
‖
c
‖
lim
‖
x
−
a
‖
→
0
‖
f
(
x
)
−
b
‖
=
0
⋅
0
+
‖
b
‖
⋅
0
+
‖
c
‖
⋅
0
=
0
{\displaystyle {\begin{array}{l}{\Big |}{\big (}\mathbf {f} \cdot \mathbf {g} {\big )}{\big (}\mathbf {x} {\big )}-\mathbf {b} \cdot \mathbf {c} {\Big |}\leqslant {\Big \|}\mathbf {f} {\big (}\mathbf {x} {\big )}-\mathbf {b} {\Big \|}\cdot {\Big \|}\mathbf {g} {\big (}\mathbf {x} {\big )}-\mathbf {c} {\Big \|}+{\big \|}\mathbf {b} {\big \|}\cdot {\Big \|}\mathbf {g} {\big (}\mathbf {x} {\big )}-\mathbf {c} {\Big \|}+{\big \|}\mathbf {c} {\big \|}\cdot {\Big \|}\mathbf {f} {\big (}\mathbf {x} {\big )}-\mathbf {b} {\Big \|}\Rightarrow \\0\leqslant \lim _{\big \|}\mathbf {x} -\mathbf {a} {\big \|}\to 0}{\Big |}{\big (}\mathbf {f} \cdot \mathbf {g} {\big )}{\big (}\mathbf {x} {\big )}-\mathbf {b} \cdot \mathbf {c} {\Big |}\leqslant \lim _{\big \|}\mathbf {x} -\mathbf {a} {\big \|}\to 0}{\Big \|}\mathbf {f} {\big (}\mathbf {x} {\big )}-\mathbf {b} {\Big \|}\cdot \lim _{\big \|}\mathbf {x} -\mathbf {a} {\big \|}\to 0}{\Big \|}\mathbf {g} {\big (}\mathbf {x} {\big )}-\mathbf {c} {\Big \|}+\\{\big \|}\mathbf {b} {\big \|}\cdot \lim _{\big \|}\mathbf {x} -\mathbf {a} {\big \|}\to 0}{\Big \|}\mathbf {g} {\big (}\mathbf {x} {\big )}-\mathbf {c} {\Big \|}+{\big \|}\mathbf {c} {\big \|}\lim _{\big \|}\mathbf {x} -\mathbf {a} {\big \|}\to 0}{\Big \|}\mathbf {f} {\big (}\mathbf {x} {\big )}-\mathbf {b} {\Big \|}=0\cdot 0+{\big \|}\mathbf {b} {\big \|}\cdot 0+{\big \|}\mathbf {c} {\big \|}\cdot 0=\\0\end{array}
, como queríamos demostrar.
d
)
g
(
x
)
=
f
(
x
)
,
c
=
b
⇒
lim
x
→
a
‖
f
(
x
)
‖
2
=
‖
b
‖
2
{\displaystyle d)\quad \mathbf {g} {\big (}\mathbf {x} {\big )}=\mathbf {f} {\big (}\mathbf {x} {\big )},\mathbf {c} =\mathbf {b} \Rightarrow \lim _{\mathbf {x} \to \mathbf {a} }{\Big \|}\mathbf {f} {\big (}\mathbf {x} {\big )}{\Big \|}^{2}={\big \|}\mathbf {b} {\big \|}^{2}
, como queríamos demostrar.
Demostración
Sean
y
=
g
(
x
)
{\displaystyle \mathbf {y} =\mathbf {g} {\big (}\mathbf {x} {\big )}
y
b
=
g
(
a
)
{\displaystyle \mathbf {b} =\mathbf {g} {\big (}\mathbf {a} {\big )}
. Entós, :
lim
‖
x
−
a
‖
→
0
‖
f
[
g
(
x
)
]
−
f
[
g
(
a
)
]
‖
=
lim
‖
y
−
b
‖
→
0
‖
f
(
y
)
−
f
(
b
)
‖
=
0
⇒
lim
x
→
a
f
[
g
(
x
)
]
=
f
[
g
(
a
)
]
{\displaystyle {\begin{array}{l}\lim _{\big \|}\mathbf {x} -\mathbf {a} {\big \|}\to 0}{\Big \|}\mathbf {f} {\Big [}\mathbf {g} {\big (}\mathbf {x} {\big )}{\Big ]}-\mathbf {f} {\Big [}\mathbf {g} {\big (}\mathbf {a} {\big )}{\Big ]}{\Big \|}=\lim _{\big \|}\mathbf {y} -\mathbf {b} {\big \|}\to 0}{\Big \|}\mathbf {f} {\big (}\mathbf {y} {\big )}-\mathbf {f} {\big (}\mathbf {b} {\big )}{\Big \|}=0\Rightarrow \\\lim _{\mathbf {x} \to \mathbf {a} }\mathbf {f} {\Big [}\mathbf {g} {\big (}\mathbf {x} {\big )}{\Big ]}=\mathbf {f} {\Big [}\mathbf {g} {\big (}\mathbf {a} {\big )}{\Big ]}\end{array}
como queríamos demostrar.
Derivaes direccionales
Derivada d'un campu angular al respective de un vector
Derivaes parciales
∂
f
∂
x
k
=
lim
h
→
0
f
(
x
1
,
…
,
x
k
+
h
,
…
,
x
n
)
−
f
(
x
1
,
…
,
x
k
,
…
,
x
n
)
h
{\displaystyle {\cfrac {\partial f}{\partial x_{k}=\lim _{h\to 0}{\cfrac {f{\big (}x_{1},\ldots ,x_{k}+h,\ldots ,x_{n}{\big )}-f{\big (}x_{1},\ldots ,x_{k},\ldots ,x_{n}{\big )}{h}
Si derivamos la espresión anterior al respective de una segunda variable,
x
j
{\displaystyle x_{j}
, vamos tener
∂
2
f
∂
x
j
∂
x
k
{\displaystyle {\cfrac {\partial ^{2}f}{\partial x_{j}\partial x_{k}
. Na práutica, vamos calcular
∂
f
∂
x
k
{\displaystyle {\cfrac {\partial f}{\partial x_{k}
derivando al respective de
x
k
{\displaystyle x_{k}
y suponiendo
x
j
,
∀
j
≠
k
{\displaystyle x_{j},\quad \forall j\neq k}
constante.
La diferencial
Definición de campu angular diferenciable
L'anterior ecuación ye la fórmula de Taylor de primer orde pa
f
(
a
+
v
)
{\displaystyle f{\big (}\mathbf {a} +\mathbf {v} {\big )}
.
Teorema d'unicidá de la diferencial
Demostración
a
)
v
=
h
y
,
h
∈
R
,
lim
‖
v
‖
→
0
f
(
x
+
v
)
=
lim
‖
v
‖
→
0
f
(
x
+
h
y
)
=
f
(
x
)
+
f
L
(
h
y
)
=
f
(
x
)
+
h
f
L
(
y
)
⇒
lim
h
→
0
f
(
x
+
h
y
)
−
f
(
x
)
h
=
f
′
(
x
;
y
)
=
f
L
(
y
)
{\displaystyle {\begin{array}{rl}a)&\mathbf {v} =h\mathbf {y} ,\quad h\in \mathbb {R} ,\\&\lim _{\big \|}\mathbf {v} {\big \|}\to \mathbf {0} }f{\big (}\mathbf {x} +\mathbf {v} {\big )}=\lim _{\big \|}\mathbf {v} {\big \|}\to \mathbf {0} }f{\big (}\mathbf {x} +h\mathbf {y} {\big )}=f{\big (}\mathbf {x} {\big )}+f_{L}{\big (}h\mathbf {y} {\big )}=\\&f{\big (}\mathbf {x} {\big )}+hf_{L}{\big (}\mathbf {y} {\big )}\Rightarrow \\&\lim _{h\to 0}{\cfrac {f{\big (}\mathbf {x} +h\mathbf {y} {\big )}-f{\big (}\mathbf {x} {\big )}{h}=f'{\big (}\mathbf {x} ;\mathbf {y} {\big )}=f_{L}{\big (}\mathbf {y} {\big )}\end{array}
como queríamos demostrar.
b
)
{\displaystyle b)}
Espresando
y
{\displaystyle y}
en función de los sos componentes na base :
{
y
1
,
…
,
y
n
}
,
f
L
(
y
)
=
f
L
(
∑
k
=
1
n
y
k
y
k
)
=
∑
k
=
1
n
y
k
f
L
(
y
k
)
=
∑
k
=
1
n
y
k
f
′
(
x
;
y
k
)
=
∑
k
=
1
n
y
k
∂
f
∂
x
k
{\displaystyle {\begin{array}{l}{\big \{}\mathbf {y} _{1},\ldots ,\mathbf {y} _{n}{\big \},f_{L}{\big (}\mathbf {y} {\big )}=f_{L}{\big (}\sum _{k=1}^{n}y_{k}\mathbf {y} _{k}{\big )}=\sum _{k=1}^{n}y_{k}f_{L}{\big (}\mathbf {y} _{k}{\big )}=\sum _{k=1}^{n}y_{k}f'{\big (}\mathbf {x} ;\mathbf {y} _{k}{\big )}=\\\sum _{k=1}^{n}y_{k}{\cfrac {\partial f}{\partial x_{k}\end{array}
como queríamos demostrar.
Regla de la cadena
Diferencial d'un campu vectorial
Espresando
f
′
(
x
;
y
)
{\displaystyle \mathbf {f'} {\big (}\mathbf {x} ;\mathbf {y} {\big )}
en función de los sos componentes, tenemos
f
′
(
x
;
y
)
=
[
f
1
′
(
x
;
y
)
,
…
,
f
m
′
(
x
;
y
)
]
{\displaystyle \mathbf {f'} {\big (}\mathbf {x} ;\mathbf {y} {\big )}={\Big [}f'_{1}{\big (}\mathbf {x} ;\mathbf {y} {\big )},\ldots ,f'_{m}{\big (}\mathbf {x} ;\mathbf {y} {\big )}{\Big ]}
Esta ye la fórmula de Taylor de primer orde pa
f
.
f
L
(
v
)
=
f
′
(
x
;
v
)
{\displaystyle \mathbf {f} .\quad \mathbf {f} _{L}{\big (}\mathbf {v} {\big )}=\mathbf {f} '{\big (}\mathbf {x} ;\mathbf {v} {\big )}
.
La matriz de
f
′
{\displaystyle \mathbf {f} '}
ye'l so matriz jacobiana.
Diferenciabilidad implica continuidá
Deduzse fácilmente de la fórmula de Taylor de primer orde yá vista.
Regla de la cadena pa diferenciales de campos vectoriales
Condición abonda pa la igualdá de les derivaes parciales mistes
∂
2
f
∂
x
i
∂
x
j
=
∂
2
f
∂
x
j
∂
x
i
∀
i
≠
j
⇔
{\displaystyle {\cfrac {\partial ^{2}f}{\partial x_{i}\partial x_{j}={\cfrac {\partial ^{2}f}{\partial x_{j}\partial x_{i}\quad \forall i\neq j\Leftrightarrow }
dambes derivaes parciales esisten y son continues en
x
{\displaystyle \mathbf {x} }
.
Aplicaciones del cálculu diferencial
Cálculu de máximos, mínimos y puntos de ensilladura pa campos angulares
Un campu angular tien un máximu en
x
=
a
⇔
{\displaystyle \mathbf {x} =\mathbf {a} \Leftrightarrow }
esiste una n-bola
B
(
a
)
|
∀
x
∈
B
(
a
)
f
(
x
)
⩽
f
(
a
)
{\displaystyle B{\big (}\mathbf {a} {\big )}{\Big |}\forall \mathbf {x} \in B{\big (}\mathbf {a} {\big )}\quad f{\big (}\mathbf {x} {\big )}\leqslant f{\big (}\mathbf {a} {\big )}
Un campu angular tien un mínimu en
x
=
a
⇔
{\displaystyle \mathbf {x} =\mathbf {a} \Leftrightarrow }
esiste una n-bola
B
(
a
)
|
∀
x
∈
B
(
a
)
f
(
x
)
⩾
f
(
a
)
{\displaystyle B{\big (}\mathbf {a} {\big )}{\Big |}\forall \mathbf {x} \in B{\big (}\mathbf {a} {\big )}\quad f{\big (}\mathbf {x} {\big )}\geqslant f{\big (}\mathbf {a} {\big )}
Un campu angular tien un puntu de ensilladura
⇔
{\displaystyle \Leftrightarrow }
∀
B
(
a
)
∃
x
|
f
(
x
)
⩽
f
(
a
)
∧
∃
x
|
f
(
x
)
⩾
f
(
a
)
{\displaystyle \forall B{\big (}\mathbf {a} {\big )}\quad \exists \mathbf {x} {\big |}f{\big (}\mathbf {x} {\big )}\leqslant f{\big (}\mathbf {a} {\big )}\land \exists \mathbf {x} {\big |}f{\big (}\mathbf {x} {\big )}\geqslant f{\big (}\mathbf {a} {\big )}
.
Función con un puntu de ensilladura
Pa saber si ye unu de los casos anteriores:
Llogramos
x
|
∂
f
∂
x
k
=
0
∀
k
|
1
⩽
k
⩽
n
{\displaystyle \mathbf {x} {\Big |}{\cfrac {\partial f}{\partial x_{k}=0\qquad \forall k{\Big |}1\leqslant k\leqslant n}
Llogramos la matriz hessiana de f. Sía esta
F
(
x
)
{\displaystyle \mathbf {F} {\big (}\mathbf {x} {\big )}
.
F
(
x
)
{\displaystyle \mathbf {F} {\big (}\mathbf {x} {\big )}
ye definida positiva
⇒
f
{\displaystyle \Rightarrow f}
tien un mínimu local (mínimu relativu ) en
x
{\displaystyle \mathbf {x} }
.
F
(
x
)
{\displaystyle \mathbf {F} {\big (}\mathbf {x} {\big )}
ye definida negativa
⇒
f
{\displaystyle \Rightarrow f}
tien un máximu local (máximu relativu ) en
x
{\displaystyle \mathbf {x} }
.
F
(
x
)
{\displaystyle \mathbf {F} {\big (}\mathbf {x} {\big )}
ye indefinida
⇒
f
{\displaystyle \Rightarrow f}
tien un puntu de ensilladura en
x
{\displaystyle \mathbf {x} }
.
No enantes espuesto, supunximos que
∂
2
f
∂
x
i
∂
x
j
{\displaystyle {\cfrac {\partial ^{2}f}{\partial x_{i}\partial x_{j}
ye continua
∀
i
,
j
|
1
⩽
i
⩽
n
,
1
⩽
j
⩽
n
{\displaystyle \forall i,j{\big |}1\leqslant i\leqslant n,1\leqslant j\leqslant n}
Ver tamién
Función diferenciable
Campu angular
Campu vectorial
Gradiente
Diverxencia
Rotacional
Integral de llinia
Integral de superficie
Integral múltiple
Multiplicadores de Lagrange
Referencies