x <- as.matrix(c(1,3,2))
x
## [,1]
## [1,] 1
## [2,] 3
## [3,] 2
y <- as.matrix(c(-2,1,1))
y
## [,1]
## [1,] -2
## [2,] 1
## [3,] 1
z <- 3*x
z
## [,1]
## [1,] 3
## [2,] 9
## [3,] 6
w <- x + y
w
## [,1]
## [1,] -1
## [2,] 4
## [3,] 3
norm(x, type="F")
## [1] 3.741657
norm(z, type="F")
## [1] 11.22497
x1 <-as.matrix(c(1,2,1))
x1
## [,1]
## [1,] 1
## [2,] 2
## [3,] 1
x2 <-as.matrix(c(1,0,-1))
x2
## [,1]
## [1,] 1
## [2,] 0
## [3,] -1
x3 <-as.matrix(c(1,-2,1))
x3
## [,1]
## [1,] 1
## [2,] -2
## [3,] 1
X <-cbind(x1,x2,x3)
X
## [,1] [,2] [,3]
## [1,] 1 1 1
## [2,] 2 0 -2
## [3,] 1 -1 1
det(X)
## [1] -8
det(X)==0
## [1] FALSE
False, hence x1, x2, x3 is linearly independent
A <-rbind(c(0,3,1), c(1,-1,1))
A
## [,1] [,2] [,3]
## [1,] 0 3 1
## [2,] 1 -1 1
B <- rbind(c(1,-2,-3), c(2,5,1))
B
## [,1] [,2] [,3]
## [1,] 1 -2 -3
## [2,] 2 5 1
4*A
## [,1] [,2] [,3]
## [1,] 0 12 4
## [2,] 4 -4 4
A+B
## [,1] [,2] [,3]
## [1,] 1 1 -2
## [2,] 3 4 2
A <-rbind(c(3,-1,2), c(1,5,4))
A
## [,1] [,2] [,3]
## [1,] 3 -1 2
## [2,] 1 5 4
B <- as.matrix(c(-2,7,9))
B
## [,1]
## [1,] -2
## [2,] 7
## [3,] 9
C <-rbind(c(2,0), c(1,-1))
C
## [,1] [,2]
## [1,] 2 0
## [2,] 1 -1
A%*%B
## [,1]
## [1,] 5
## [2,] 69
C%*%A
## [,1] [,2] [,3]
## [1,] 6 -2 4
## [2,] 2 -6 -2
A <- rbind(c(1,-2,3), c(2,4,-1))
A
## [,1] [,2] [,3]
## [1,] 1 -2 3
## [2,] 2 4 -1
b <- as.matrix(c(7,-3, 6))
b
## [,1]
## [1,] 7
## [2,] -3
## [3,] 6
c <- as.matrix(c(5,8,-4))
c
## [,1]
## [1,] 5
## [2,] 8
## [3,] -4
d <-as.matrix(c(2,9))
A%*%b
## [,1]
## [1,] 31
## [2,] -4
b%*%t(c)
## [,1] [,2] [,3]
## [1,] 35 56 -28
## [2,] -15 -24 12
## [3,] 30 48 -24
t(b)%*%c
## [,1]
## [1,] -13
t(d)%*%A%*%d
## Error in t(d) %*% A %*% d: non-conformable arguments
A=rbind(c(3,2), c(4,1))
A
## [,1] [,2]
## [1,] 3 2
## [2,] 4 1
B <- solve(A)
A%*%B
## [,1] [,2]
## [1,] 1 0
## [2,] 0 1
B%*%A
## [,1] [,2]
## [1,] 1 0
## [2,] 0 1
A <-rbind(c(1,-5), c(-5,1))
A
## [,1] [,2]
## [1,] 1 -5
## [2,] -5 1
eigen(A)
## eigen() decomposition
## $values
## [1] 6 -4
##
## $vectors
## [,1] [,2]
## [1,] -0.7071068 -0.7071068
## [2,] 0.7071068 -0.7071068
A =rbind(c(13, -4,2), c(-4,13,-2),c(2,-2,10))
A
## [,1] [,2] [,3]
## [1,] 13 -4 2
## [2,] -4 13 -2
## [3,] 2 -2 10
Eigen_A<-eigen(A)
Eigen_A$values
## [1] 18 9 9
Eigen_A$vectors
## [,1] [,2] [,3]
## [1,] 0.6666667 -0.7453560 0.0000000
## [2,] -0.6666667 -0.5962848 0.4472136
## [3,] 0.3333333 0.2981424 0.8944272
A%*%Eigen_A$vectors[,1]
## [,1]
## [1,] 12
## [2,] -12
## [3,] 6
A%*%Eigen_A$vectors[,2]
## [,1]
## [1,] -6.708204
## [2,] -5.366563
## [3,] 2.683282
A%*%Eigen_A$vectors[,3]
## [,1]
## [1,] 0.000000
## [2,] 4.024922
## [3,] 8.049845
Eigen_A$vectors%*%t(Eigen_A$vectors)
## [,1] [,2] [,3]
## [1,] 1.000000e+00 -2.220446e-16 5.551115e-17
## [2,] -2.220446e-16 1.000000e+00 5.551115e-17
## [3,] 5.551115e-17 5.551115e-17 1.000000e+00
t(Eigen_A$vectors)%*%Eigen_A$vectors
## [,1] [,2] [,3]
## [1,] 1.000000e+00 -2.359224e-16 0
## [2,] -2.359224e-16 1.000000e+00 0
## [3,] 0.000000e+00 0.000000e+00 1
A <- rbind(c(3, -sqrt(2)), c(-sqrt(2), 2))
A
## [,1] [,2]
## [1,] 3.000000 -1.414214
## [2,] -1.414214 2.000000
\[ (x_1, x_2) \times A \times \left( \begin{array}{c} x_1 \\ x_2 \end{array} \right) = = 3 x_1^2+2x_2^2-2\sqrt{2}x_1x_2\]
eigen(A)
## eigen() decomposition
## $values
## [1] 4 1
##
## $vectors
## [,1] [,2]
## [1,] -0.8164966 -0.5773503
## [2,] 0.5773503 -0.8164966
\[ (x_1, x_2) \times A \times \left( \begin{array}{c} x_1 \\ x_2 \end{array} \right) = 4(x_1, x_2) \times e_1 e_1' \times \left( \begin{array}{c} x_1 \\ x_2 \end{array} \right) + (x_1, x_2) \times e_2 e_2' \times \left( \begin{array}{c} x_1 \\ x_2 \end{array} \right) = 4 ((x_1, x_2) e_1)^2 + ((x_1, x_2) e_2)^2 >0,\] if \((x_1,x_2) \ne (0,0).\)