Title | MA2001 cheatsheet |
---|---|
Course | Linear Algebra I |
Institution | National University of Singapore |
Pages | 10 |
File Size | 1.3 MB |
File Type | |
Total Downloads | 91 |
Total Views | 143 |
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cRi , c =0 {(1 + s, 2s, s)∣s ∈ R}
{
Ri ↔ Rj Ri + cRj , c ∈ R
x=t = 2t − 1
x1 , x2 , … , xn = 0
Ri
(A T )T = A B=C
E 3 E 2 E1 A = B 1 1 A = E1 1 E2 E3 B
(A + B) T = AT + B T A
B2 m× B1 = B 2
AB1 = AB2
C1
C2 m× C1 = C2
C1 A = C2 A
a A=[ c A 1 =
(AB)T
A=[
b ] d
1 d [ ad bc c
(cA)T = cAT
c
B1
b ] a
T
=B A
a c
T
b ] d
A
det(A) = ad bc =0
A
ifE isanelementarymatrixofthesamesizeasA, det(B) = det(E) det(A) = det(EA) kR
AB R ↔R m
A, B
c
R +kRm
A B
(cA)1 = 1c A 1
cA A
A B 1
T 1
T
(A )
AB
(AB)1 = B 1 A1
det(B) = det(A)
det(B) = det(A)
A, B det(cA) = A = 0
(A )1 = (A1 )
det(A)
det(AB) = det(A) det(B )
AB = I
A, B
c
det(A1 ) =
B
1 det(A)
B1 = A
BA = I ifAisaninvertiblematrix,then 1 A 1 = adj (A) det(A)
A, B A AB
AB A
det(A) = det(AT )
BA B
i = A
det(Ai ) b
det(A) = 0
det(Ai ) det(A)
ih
det(E ) = k
;
= (A )
(A1 )1 = A
A 1 = B;
;
1 T
A1
A
det(B) = k det(A)
c
;
det(E) = det(E) = 1
S
R
V S
R
S ++ = 0 thegeneralsolutiontothelinearsystem { − + 2 = 1
LetV beasubsetofR .V isasubspaceofR if V = span(S)forsomevectors1 , 2 , … , k ∈ R .
V V
V
(, , ) = ( 12 − 32 , − 12 + 12 , )where ∈ R
(ii)(Closedunderlinearcombinations)∀, ∈ V , , ∈ R, + ∈ V
{(, , ) ∣ + + = 0and − + 2 =
V
S
thecoordinatevectorofV relativetoS , () = (c1 , c2 , … , ck ) ∈ Rk
1, 2, … , k
[] V
∈ V ⊆ R R
V
V V
V W
V
dim(V ),
W ⊆
W
1, 2, … , k V
()
V
R W
S V
{( 12 − 32 , −21 + 12 , ) ∣ ∈ R}
V = R
(i)(Containstheorigin)O ∈ V
∅
1}
V
AsubspaceV ⊆ R
=
() ∈ Rk
k
dim(R ) =
E = {e 1 , e2 , … , e } e1 = (1, 0, … , 0), e2 = (0, 1, … , 0), e = (0, 0, … , 1) LetS = {1 , 2 , ⋯ , } ⊆ R . 0 ∈ span(S ) ∀ 1 , 2 , … , ∈ span(S)
R
V
c1 , c2 , … , c ∈ R,
c 1 1 + c2 2 + ⋯ + c ∈ span(S )
()E = (1 , 2 , … , ) =
⎡ ⎢ ⎢ ⎢ ⎣
S
S
V
∣S∣ = k
S 1 , 2 , … , ∈ V
c 1, c 2, … , c ∈ R
(c1 1 +c 2 2 + ⋯ + c ) S = c1 (1 )S + c2 (2 )S + ⋯ + c ( )S
S S
V
V
∣S∣ = k
S
, ∈ V , = () S = () S
S
V S
letS = {1 , 2 , … , } a(S) = R thelinearsystem 1 k1 2 k2 isconsistent∀k1 , k2 , … , k ∈ R ⋮ ⋮ k
k
S
V
V
∣S∣ = k
REFhasnozerorow ⇒ a(S) = R V
1 , 2 , … , (1 )S , (2 )S , … , ( )S Rk
LetS = {1 , 2 , ⋯ , k } ⊆ R . k < ⇒ a(S) = R
LetU beasubspaceofvectorspaceV . Then dim(U ) ≤ dim(V ). If dim(U ) = dim(V )thenU = V .
R2 R
Rk
3
T
span{1 , 2 , … , } = V span{(1 )S , (2 )S , … , ( )S } = Rk
∣T ∣ > dim(V )
V
span{1 , 2 , 3 } ⊆ span{1 , 2 } 1 , 2
1 , 2 , 3
P = [ [1 ]T [ 2] T {1 , 2 , … , k }
A
[ 1 2 ∣ 1 ∣ 2 ∣ 3 ] span{1 , 2 , 3 } ⊆ V
[ T
A = 0
1 , 2
V
if 1 , 2 , … , ∈ a(S) ⇒ a{1 , 2 , … , } ⊆ a(S )
P P −1
det(A) =0
A=B
A
A ⊆ B∧B ⊆ A
c 1 1 + c2 2 + ⋯ + ck k = 0 S = {0} S S
R A
R
(∗) k 1 , 2 , … , k−1 , a{1 , 2 , … , k−1 } = a{1 , 2 , … , k−1 , k }
∣
⋯
[k ]T ]
G-JElimination
S ] [ I []T = P []S S
T T
S
S=
∣
P ]
R
A
= span{1 , 2 , , } R = columnspaceofAT ⎡ 1 ⎤
A
whereA = ⎢ , i = [ai1 ⎣ orA = [c1
A A = 0
ai2
ai ] , rank(A) = min{, }
⎡ a 1 ⎤ c ] , c =⎢ ⎣ a R
A = span{c1 , c2 , , c } R = rowspaceofAT
nullity(A) dim(R ) =
A
det(A) =0
rank(A T ) + nullity(AT ) = numberofrowsinA rank(A) + nullity(A) = numberofcolumnsinA
rank(0) = 0,
rank(I ) = ,
rank(A) min{, }
rank(A) = AT
A
rank(AB) min{rank(A), rank(B )}
= {A R } A = b b
A
A
(A b ) A = b
A
{0} A = b
= { + isanelementofthenullspaceofA} ✓
A
nullity(A) = dim(nullspaceofA)
d(u, v) = u v u =
u v = 0,
uu =
u21
+
u22
++
u2n
=
2
Rn
0
u v = uv T = ∑ni=1 ui vi = u1 v1 + u 2v 2 + + un vn u = cos 1 (
v
uv uv )
=
u2 +v 2 uv 2 cos 1( 2uv
⇏
)
2 ++un vn ) Rn : = cos1 ( u1 v1 +u2uv v
u v2 = u2 + v 2 2uv cos
E = e1 , e2 , , en
u Rn
Rn
uv =vu
S
(cu) v = u (cv) = c(u v )
u
0 Rn
V V
w (u + v) = w u + w v
V
V
S S = dim(V )
V
u1 , u2 , , uk
foranyw Rn , w u1 w u2 w uk u1 + u2 + + uk u1 u1 u2 u2 uk uk istheprojectionofwontoV .
span(S) = V
(u v) w = u (v w ) cu = cu uu0 uu=0 u=0 u v uv u + v u + v
LetS = u1 , u2 , , un beanorthogonalbasisforV . Thenforanyw V , w u2 w uk w u1 w= u1 + uk u2 + + uk uk u 2 u2 u1 u1 w u1 w u2 w uk , ,, ) (w)S = ( u 1 u1 u 2 u 2 uk uk
d(u, w) d(u, v) + d(v, w) LetS = u1 , u2 , , un beanorthonormalbasisforV . Thenforanyw V , w = (w v1 )v1 + (w v2 )v2 + + (w vk )vk (w)S = (w v1 , w v 2, , w vk )
Rn
V
v1 , v2 , , vk
V foranyw Rn , (w u1 )u1 + (w u2 )u2 + + (w uk )uk istheprojectionofwontoV .
u Rn
Ax = b
p = Au
b
p = Au
b
A
Ax = b
u A
E = e 1, e 2
S = u1 , u2 x, y, x , y
a1 , a2 , , an A =
b = Au [a1 a2 an ]
u1 = (c , in ) = e1 c + e2 in T
u
T
u2 = ( in , c ) = e1 in + e2 c
A Ax = A b
S
b
E
c in ] P =[ in c
Ax = p A
x p
PT AT Ax p
u d(u, p) d(u, v)
E
T
=A b
xy
V
v = (x, y) R2 ,
fallv V
b Au b Av
A
fallv Rn
e1 , e2 , u1 , u2
b AT Ax = A T b
u
S
x y (v)S = (x , y )
x c in x [ ] = [ v ] S = P T [ v ]E = [ ][ ] in c y y x = x c + y in y = x in + y c
Au u A A1 = AT
Rn
A A
S
T
P
S
T
P P T = P 1 =
T
S
Rn
A
n
E (A)
E u Rn
Au = u
u
A A
1 ⎡ 1
A
Au = u
u
Au spanu u, v, w [ u v w ] 1A[ u v w ] =
Rn u
A
m
⎡ 1 ⎢ 0 m A = P⎢ ⎢ ⎣ 0
P P A
1
A A
det(I A) = 0 det(I A)
0 1 2
0
0 m 2
0
0 ⎤ 0 1 P 1 n
0 ⎤ 0 Pm m n
AP
P
u Rn \0 (I A)u = 0 det(I A) = 0
0 = P⎢ ⎢ ⎢ ⎣ 0
i
m Z+
E u
1
(I A)x = 0
[ u v w ]
i =0
A
A
nn
A A
A
n
P T AP = D D
P
P
A
AT = A
A A
det(I A) = ( 1 )r 1 ( 2)r2 ( k ) rk here 1 , , k aredistincteigenaluesofA. Aisdiagonaliable dim(E i ) = ri foreacheigenaluei S i = ri
Ax = 0
r 1 + r2 + + r k = n det(A) =0
ri Rn
A
Rn
A rank(A) = n 0
A
A
n
A
R R T : R R
: R R =
T (0) = 0
R
T
⎛⎡ 1 ⎞ ⎡ a 11 a 12 a1 ⎡ 1 ⎜⎢ 2 ⎟ ⎢ a 21 a 22 a2 ⎢ 2 T ⎜⎢ ⎟ = ⎢ ⎢ ⎜⎢ ⎟ ⎢ ⎢ ⎝ ⎠ a1 a2 a ⎡ a11 1 a 12 2 a 1 ⎢ a21 1 a 22 2 a 2 = ⎢ ⎢ a1 1 a 2 2 a for( 1, 2, , )T R
(aij )
T
1 , 2 , , R
c 1, c 2, , c R
T (c 1 1 + c2 2 + + c ) = c1 T (1 ) + c 2 T (2 ) + + c T ( ) T R(T )
T :R R
T
R(T ) = {T () R } R
A
[T (e1 ) T (e2 )
⎡ a 1j ⎢ a 2j = T (ei ) = Ae i = ⎢ ⎢ ai
T (e )]
R(T ) = span{T (1 ), T (2 ), , T ( )} R(T ) =
ih
A R(T )
T
A
rank(T ) = dim(R(T )) = dim(columnspaceofA) = rank(A)
R V
W T :V W
T (c + d ) = cT () + dT ()
, V
c, d R
let{1 , 2 , , }beabasisforR . foranyvector R , = c1 1 + c2 2 + + c forsomec1 , , c R {1 , 2 , , } T () T (1 ), T (2 ), , T ( )
I : R R I
T ker(T ) R
I
ker(T ) = { T () = 0} R
R
I
O : R R O
0
R
T S (T S )() = T (S ())
R
R
R
R (T S )() = T (S ()) = T (A) = BA BA
ker(T ) = T
A ker(T )
nullity(T ) = dim(ker(T )) = nullity(A)
T S rank(T ) + nullity(T ) = = rank(A) + nullity(A) = numberofcolumnsinA...