# CS代考计算机代写 matlab flex algorithm STAT 513/413: Lecture 4 Mostly linear algebra

STAT 513/413: Lecture 4 Mostly linear algebra
(first non-trivialities perhaps)

A tale of expert code I: floating point arithmetics
Floating-point arithmetics: numbers are represented as
base ∗ 10exponent – which has inevitable consequences
> 0.000001*1000000
[1] 1
> x=0; for (k in (1:1000000)) x=x+0.000001 >x
[1] 1
> x-1
[1] 7.918111e-12
> x=1000000; for (k in 1:1000000) x=x+0.000001 >x
[1] 1000001
> x-1000000
[1] 1.000008
> x-1000001
[1] 7.614493e-06
The moral here is: with floating-point arithmetics, adding works well if the added numbers are about of the same magnitude
1

A better algorithm thus does it
> x=0; for (k in (1:1000000)) x=x+0.000001; x=x+1000000 >x
[1] 1000001
> x-1000000
[1] 1
> x-1000001
[1] 0
Yeah, but what to do in general? The solution seems to be: use addition programmed by experts
> sum
function (…, na.rm = FALSE) .Primitive(“sum”)
> x=sum(c(1000000,rep(0.000001,1000000))) >x
[1] 1000001
> x-1000000
[1] 1
> x-1000001
[1] -2.561137e-09
2

Vectorization alone does not do it
> x=rep(1,1000001) %*% c(1000000,rep(0.000001,1000000))
> x-1000000
[,1]
[1,] 1.000008
> x-1000001
[,1]
[1,] 7.614493e-06
> x=crossprod(rep(1,1000001),c(1000000,rep(0.000001,1000000)))
> x-1000000
[,1]
[1,] 1.000008
> x-1000001
[,1]
[1,] 7.614493e-06
3

A tale of expert code II: never invert a matrix…
The theory for a linear model y ∼ Xβ suggests that you obtain the least squares estimates via the formula
b = (XTX)−1XTy
However, in computing you are never ever (well, every rule has an
exception, but still) supposed to do
b <- solve(t(X) %*% X) %*% t(X) %*% y Doing alternatively b <- solve(crossprod(X)) %*% crossprod(X, y) does not really save it 4 ... but rather solve (a system of) equations It is much better to get b via solving the system (XTX)b = XTy To this end, b <- solve(crossprod(X), crossprod(X, y)) may work pretty well; but experts know that the best way is via a so- called QR decomposition (MATLAB “backslash” operator), which in R amounts to b <- qr.solve(X, y) This is correct - but many people do not need to know that much; unless they are in certain special situations), they may just do b <- coef(lm(y ~ X-1)) and it amounts to the same thing! 5 Showing the difference is, however, a bit intricate... ...because the numerics of R is very good... The first attempt > x = runif(10,0,10)
> X = cbind(rep(1,length(x)),x)
> y = 2 + 3*x + rnorm(length(x))
> cbind(X, y)
xy [1,] 1 5.088385 18.846518 [2,] 1 1.875540 8.434781 [3,] 1 4.509448 16.397015 [4,] 1 7.366187 24.432547 [5,] 1 4.914751 18.399520 [6,] 1 9.296908 29.273038 [7,] 1 8.083712 26.970036 [8,] 1 5.210684 16.587565 [9,] 1 5.028429 15.727741 [10,] 1 8.422086 28.772038
6

The first attempt actually does not show anything
> solve(t(X) %*% X) %*% t(X) %*% y
[,1]
2.715395
x 2.954821
> solve(crossprod(X)) %*% crossprod(X, y)
[,1]
2.715395
x 2.954821
> solve(crossprod(X), crossprod(X, y))
[,1]
2.715395
x 2.954821
> qr.solve(X, y)
x
2.715395 2.954821
> coef(lm(y~X-1))
X Xx
2.715395 2.954821
7

We have to be more extreme
> x = rep(1,1000)+rnorm(1000,0,.000001)
> X = cbind(rep(1,length(x)),x)
> y = 2 + 3*x + rnorm(length(x))
> det(crossprod(X))
[1] 1.133003e-06
> solve(t(X) %*% X) %*% t(X) %*% y
[,1]
20737.20
x -20732.22
> solve(crossprod(X), crossprod(X, y))
[,1]
20737.19
x -20732.21
> qr.solve(X, y)
x
20733.83 -20728.85
> coef(lm(y ~ X-1))
X Xx
20733.83 -20728.85
8

*
%*%
crossprod(A,B)
crossprod(A)
rep()
solve(A, y)
solve(A)
c()
matrix()
rbind(A,B)
cbind(A,B)
length()
dim()
is for componentwise multiplication (components better match!)
vector/matrix multiplication ATB (uses dedicated algorithm)
in particular ATA
a repetition function, very flexible finds b such that Ab = y
finds A−1 (if needed be) concatenation of vectors, flexible too
setting up matrices
matrices are merged by rows (must match) matrices are merged by columns (must match) returns the length of a vector
returns the dimension of a matrix
Vector and matrix algebra
9

Type conversions
General format as.type > qr.solve(X, y)
x
20733.83 -20728.85
> as.vector(qr.solve(X, y))
[1] 20733.83 -20728.85
> as.vector(coef(lm(y~X-1)))
[1] 20733.83 -20728.85
> as.vector(solve(crossprod(X), crossprod(X, y)))
[1] 20737.19 -20732.21
> as.vector(solve(t(X) %*% X) %*% t(X) %*% y)
[1] 20737.20 -20732.22
Note: in R, vectors are interpreted not linewise or columnwise, but in an “ambiguous manner”: whatever suits more for a multiplication to succeed. In other words, the same square matrix can be multiplied by the same vector from both sides: X %*% a or a %*% X – which creates usually no problem, until we have an expression a %*% a which is always a number, aTa for column vectors. If we want to obtain aaT, a matrix, we need to write a %*% t(a)
10

> numeric(4)
[1] 0 0 0 0
> rep(0,4)
[1] 0 0 0 0
> rep(c(0,1),4)
[1] 0 1 0 1 0 1 0 1
> rep(c(0,1),c(3,2))
[1] 0 0 0 1 1
> X=matrix(0,nrow=2,ncol=2)
> X=matrix(1:4,nrow=2,ncol=2) >X
[,1] [,2]
[1,] 1 3
[2,] 2 4
> as.vector(X)
[1] 1 2 3 4
> as.matrix(1:4)
[,1]
[1,] 1
[2,] 2
[3,] 3
[4,] 4
Potpourri
11

Finally, reminder
Inverse of a matrix should never be computed, unless: – it is absolutely necessary to compute standard errors
– the number of right-hand sides is so much larger than n that the extra cost is insignificant
(this one is based on the following: solving two systems, Ax = b1 and Ax = b2 costs exactly that much as solving one system Ax = b by first calculating A−1 and then A−1b)
– the size of n is so small that the costs are irrelevant (yeah, in the toy setting we don’t care)
(John F. Monahan, Numerical Methods of Statistics) (remarks by I.M.)
12

Some reminders from linear algebra
Useful formulae: (AB)T = BTAT
det(AB) = det(A) det(B) det(AT) = det(A)
Useful definitions: we say that matrix A is
nonnegative definite (or positive semidefinite): xTAx 􏰀 0 for every x positive definite: xTAx > 0 for every x ̸= 0
The definitions imply that A is a square matrix; some automatically require that it is also symmetric, so better check (in statistics it is almost always symmetric matrices the definitions are applied to)
Useful habit in theory (albeit not observed by R in practice): consider vectors as n × 1 columns (in statistics, it is always like this)
Useful caution: if a is an n×1 vector, then aTa is a number (which we did denote by ∥a∥2), but aaT is an n × n matrix. In general, matrix multiplication is not commutative: AB is in general different from BA
Useful principle: block matrices are multiplied in a same way as usual matrices, only blocks are itself matrices, thus multiplied as such, and hence the dimensions must match
Useful practice: check dimensions
13

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# CS代考计算机代写 matlab flex algorithm STAT 513/413: Lecture 4 Mostly linear algebra

STAT 513/413: Lecture 4 Mostly linear algebra
(first non-trivialities perhaps)

A tale of expert code I: floating point arithmetics
Floating-point arithmetics: numbers are represented as
base ∗ 10exponent – which has inevitable consequences
> 0.000001*1000000
[1] 1
> x=0; for (k in (1:1000000)) x=x+0.000001 >x
[1] 1
> x-1
[1] 7.918111e-12
> x=1000000; for (k in 1:1000000) x=x+0.000001 >x
[1] 1000001
> x-1000000
[1] 1.000008
> x-1000001
[1] 7.614493e-06
The moral here is: with floating-point arithmetics, adding works well if the added numbers are about of the same magnitude
1

A better algorithm thus does it
> x=0; for (k in (1:1000000)) x=x+0.000001; x=x+1000000 >x
[1] 1000001
> x-1000000
[1] 1
> x-1000001
[1] 0
Yeah, but what to do in general? The solution seems to be: use addition programmed by experts
> sum
function (…, na.rm = FALSE) .Primitive(“sum”)
> x=sum(c(1000000,rep(0.000001,1000000))) >x
[1] 1000001
> x-1000000
[1] 1
> x-1000001
[1] -2.561137e-09
2

Vectorization alone does not do it
> x=rep(1,1000001) %*% c(1000000,rep(0.000001,1000000))
> x-1000000
[,1]
[1,] 1.000008
> x-1000001
[,1]
[1,] 7.614493e-06
> x=crossprod(rep(1,1000001),c(1000000,rep(0.000001,1000000)))
> x-1000000
[,1]
[1,] 1.000008
> x-1000001
[,1]
[1,] 7.614493e-06
3

A tale of expert code II: never invert a matrix…
The theory for a linear model y ∼ Xβ suggests that you obtain the least squares estimates via the formula
b = (XTX)−1XTy
However, in computing you are never ever (well, every rule has an
exception, but still) supposed to do
b <- solve(t(X) %*% X) %*% t(X) %*% y Doing alternatively b <- solve(crossprod(X)) %*% crossprod(X, y) does not really save it 4 ... but rather solve (a system of) equations It is much better to get b via solving the system (XTX)b = XTy To this end, b <- solve(crossprod(X), crossprod(X, y)) may work pretty well; but experts know that the best way is via a so- called QR decomposition (MATLAB “backslash” operator), which in R amounts to b <- qr.solve(X, y) This is correct - but many people do not need to know that much; unless they are in certain special situations), they may just do b <- coef(lm(y ~ X-1)) and it amounts to the same thing! 5 Showing the difference is, however, a bit intricate... ...because the numerics of R is very good... The first attempt > x = runif(10,0,10)
> X = cbind(rep(1,length(x)),x)
> y = 2 + 3*x + rnorm(length(x))
> cbind(X, y)
xy [1,] 1 5.088385 18.846518 [2,] 1 1.875540 8.434781 [3,] 1 4.509448 16.397015 [4,] 1 7.366187 24.432547 [5,] 1 4.914751 18.399520 [6,] 1 9.296908 29.273038 [7,] 1 8.083712 26.970036 [8,] 1 5.210684 16.587565 [9,] 1 5.028429 15.727741 [10,] 1 8.422086 28.772038
6

The first attempt actually does not show anything
> solve(t(X) %*% X) %*% t(X) %*% y
[,1]
2.715395
x 2.954821
> solve(crossprod(X)) %*% crossprod(X, y)
[,1]
2.715395
x 2.954821
> solve(crossprod(X), crossprod(X, y))
[,1]
2.715395
x 2.954821
> qr.solve(X, y)
x
2.715395 2.954821
> coef(lm(y~X-1))
X Xx
2.715395 2.954821
7

We have to be more extreme
> x = rep(1,1000)+rnorm(1000,0,.000001)
> X = cbind(rep(1,length(x)),x)
> y = 2 + 3*x + rnorm(length(x))
> det(crossprod(X))
[1] 1.133003e-06
> solve(t(X) %*% X) %*% t(X) %*% y
[,1]
20737.20
x -20732.22
> solve(crossprod(X), crossprod(X, y))
[,1]
20737.19
x -20732.21
> qr.solve(X, y)
x
20733.83 -20728.85
> coef(lm(y ~ X-1))
X Xx
20733.83 -20728.85
8

*
%*%
crossprod(A,B)
crossprod(A)
rep()
solve(A, y)
solve(A)
c()
matrix()
rbind(A,B)
cbind(A,B)
length()
dim()
is for componentwise multiplication (components better match!)
vector/matrix multiplication ATB (uses dedicated algorithm)
in particular ATA
a repetition function, very flexible finds b such that Ab = y
finds A−1 (if needed be) concatenation of vectors, flexible too
setting up matrices
matrices are merged by rows (must match) matrices are merged by columns (must match) returns the length of a vector
returns the dimension of a matrix
Vector and matrix algebra
9

Type conversions
General format as.type > qr.solve(X, y)
x
20733.83 -20728.85
> as.vector(qr.solve(X, y))
[1] 20733.83 -20728.85
> as.vector(coef(lm(y~X-1)))
[1] 20733.83 -20728.85
> as.vector(solve(crossprod(X), crossprod(X, y)))
[1] 20737.19 -20732.21
> as.vector(solve(t(X) %*% X) %*% t(X) %*% y)
[1] 20737.20 -20732.22
Note: in R, vectors are interpreted not linewise or columnwise, but in an “ambiguous manner”: whatever suits more for a multiplication to succeed. In other words, the same square matrix can be multiplied by the same vector from both sides: X %*% a or a %*% X – which creates usually no problem, until we have an expression a %*% a which is always a number, aTa for column vectors. If we want to obtain aaT, a matrix, we need to write a %*% t(a)
10

> numeric(4)
[1] 0 0 0 0
> rep(0,4)
[1] 0 0 0 0
> rep(c(0,1),4)
[1] 0 1 0 1 0 1 0 1
> rep(c(0,1),c(3,2))
[1] 0 0 0 1 1
> X=matrix(0,nrow=2,ncol=2)
> X=matrix(1:4,nrow=2,ncol=2) >X
[,1] [,2]
[1,] 1 3
[2,] 2 4
> as.vector(X)
[1] 1 2 3 4
> as.matrix(1:4)
[,1]
[1,] 1
[2,] 2
[3,] 3
[4,] 4
Potpourri
11

Finally, reminder
Inverse of a matrix should never be computed, unless: – it is absolutely necessary to compute standard errors
– the number of right-hand sides is so much larger than n that the extra cost is insignificant
(this one is based on the following: solving two systems, Ax = b1 and Ax = b2 costs exactly that much as solving one system Ax = b by first calculating A−1 and then A−1b)
– the size of n is so small that the costs are irrelevant (yeah, in the toy setting we don’t care)
(John F. Monahan, Numerical Methods of Statistics) (remarks by I.M.)
12

Some reminders from linear algebra
Useful formulae: (AB)T = BTAT
det(AB) = det(A) det(B) det(AT) = det(A)
Useful definitions: we say that matrix A is
nonnegative definite (or positive semidefinite): xTAx 􏰀 0 for every x positive definite: xTAx > 0 for every x ̸= 0
The definitions imply that A is a square matrix; some automatically require that it is also symmetric, so better check (in statistics it is almost always symmetric matrices the definitions are applied to)
Useful habit in theory (albeit not observed by R in practice): consider vectors as n × 1 columns (in statistics, it is always like this)
Useful caution: if a is an n×1 vector, then aTa is a number (which we did denote by ∥a∥2), but aaT is an n × n matrix. In general, matrix multiplication is not commutative: AB is in general different from BA
Useful principle: block matrices are multiplied in a same way as usual matrices, only blocks are itself matrices, thus multiplied as such, and hence the dimensions must match
Useful practice: check dimensions
13

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