plotting matrix equation in R - r

I'm new to R and I need to plot the quadratic matrix equation:
x^T A x + b^T x + c = 0
in R^2, with A being a 2x2, b a 2x1, and c a constant. The equation is for a boundary that defines classes of points. I need to plot that boundary for x0 = -6...6, x1 = -4...6. My first thought was generate a bunch of points and see where they are zero, but it depends on the increment between the numbers (most likely I'm not going guess what points are zero).
Is there a better way than just generating a bunch of points and seeing where it is zero or multiplying it out? Any help would be much appreciated,
Thank you.

Assuming you have a symmetric matrix A,
eg
# A = | a b/2 |
# | b/2 c |
and your equation represents a conic section, you can use the conics package
What you need is a vector of coefficients c(a,b,c,d,e,f) representing
a.x^2 + b*x*y + c*y^2 + d*x + e*y + f
In your case, say you have
A <- matrix(c(2,1,1,2))
B <- c(-20,-28)
C <- 10
# create the vector
v <- append(c(diag(A),B,C),A[lower.tri(A)]*2), 1)
conicPlot(v)
You could easily wrap the multiplication out into a simple function
# note this does no checking for symmetry or validity of arguments
expand.conic <- function(A, B, C){
append(c(diag(A),B,C),A[lower.tri(A)]*2), 1)
}

Related

R Derivatives of an Inverse

I have an expression that contains several parts. However, for simplicity, consider only the following part as MWE:
Let's assume we have the inverse of a matrix Y that I want to differentiate w.r.t. x.
Y is given as I - (x * b * t(b)), where I is the identity matrix, x is a scalar, and b is a vector.
According to The Matrix Cookbook Equ. 59, the partial derivative of an inverse is:
Normally I would use the function D from the package stats to calculate the derivatives. But that is not possible in this case, because e.g. solve to specify Y as inverse and t() is not in the table of derivatives.
What is the best workaround to circumvent this problem? Are there any other recommended packages that can handle such input?
Example that doesn't work:
f0 <- expression(solve(I - (x * b %*% t(b))))
D(f0, "x")
Example that works:
f0 <- expression(x^3)
D(f0, "x")
3 * x^2
I assume that the question is how to get an explicit expression for the derivative of the inverse of Y with respect to x. In the first section we compute it and in the second section we double check it by computing it numerically and show that the two approaches give the same result.
b and the null space of b are both eigenspaces of Y which we can readily verify by noting that Yb = (1-(b'b)x)b and if z belongs to the nullspace of b then Yz = z. This also shows that the corresponding eigenvalues are 1 - x(b'b) with multiplicity 1 and 1 with multiplicity n-1 (since the nullspace of b has that dimension).
As a result of the fact that we can expand such a matrix into the sum of each eigenvalue times the projection onto its eigenspace we can express Y as the following where bb'/b'b is the projection onto the eigenspace spanned by b and the part pre-multiplying it is the eigenvalue. The remaining terms do not involve x because they involve an eigenvalue of 1 independently of x and the nullspace of b is independent of x as well.
Y = (1-x(b'b))(bb')/(b'b) + terms not involving x
The inverse of Y is formed by taking the reciprocals of the eigenvalues so:
Yinv = 1/(1-x(b'b)) * (bb')/(b'b) + terms not involving x
and the derivative of that wrt x is:
(b'b) / (1 - x(b'b))^2 * (bb')/(b'b)
Cancelling the b'b and writing the derivative in terms of R code:
1/(1 - x*sum(b*b))^2*outer(b, b)
Double check
Using specific values for b and x we can verify it against the numeric derivative as follows:
library(numDeriv)
x <- 1
b <- 1:3
# Y inverse as a function of x
Yinv <- function(x) solve(diag(3) - x * outer(b, b))
all.equal(matrix(jacobian(Yinv, x = 1), 3),
1/(1 - x*sum(b*b))^2*outer(b, b))
## [1] TRUE

While loop in R, need a more efficient code

I have written an R code to solve the following equations jointly. These are closed-form solutions that require numerical procedure.
I further divided the numerator and denominator of (B) by N to get arithmetic means.
Here is my code:
y=cbind(Sta,Zta,Ste,Zte) # combine the variables
St=as.matrix(y[,c(1,3)])
Stm=c(mean(St[,1]), mean(St[,2])); # Arithmetic means of St's
Zt=as.matrix(y[,c(2,4)])
Ztm=c(mean(Zt[,1]), mean(Zt[,2])); # Arithmetic means of Zt's
theta=c(-20, -20); # starting values for thetas
tol=c(10^-4, 10^-4);
err=c(0,0);
epscon=-0.1
while (abs(err) > tol | phicon<0) {
### A
eps = ((mean(y[,2]^2))+mean(y[,4]^2))/(-mean(y[,1]*y[,2])+theta[1]*mean(y[,2])-mean(y[,3]*y[,4])+theta[2]*mean(y[,4]))
### B
thetan = Stm + (1/eps)*Ztm
err=thetan-theta
theta=thetan
epscon=1-eps
print(c(ebs,theta))
}
Iteration does not stop as the second condition of while loop is not met, the solution is a positive epsilon. I would like to get a negative epsilon. This, I guess requires a grid search or a range of starting values for the Thetas.
Can anyone please help code this process differently and more efficiently? Or help correct my code if there are flaws in it.
Thank you
If I am right, using linearity your equations have the form
ΘA = a + b / ε
ΘB = c + d / ε
1/ε = e ΘA + f ΘB + g
This is an easy 3x3 linear system.

How do I minimize a linear least squares function in R?

I'm reading Deep Learning by Goodfellow et al. and am trying to implement gradient descent as shown in Section 4.5 Example: Linear Least Squares. This is page 92 in the hard copy of the book.
The algorithm can be viewed in detail at https://www.deeplearningbook.org/contents/numerical.html with R implementation of linear least squares on page 94.
I've tried implementing in R, and the algorithm as implemented converges on a vector, but this vector does not seem to minimize the least squares function as required. Adding epsilon to the vector in question frequently produces a "minimum" less than the minimum outputted by my program.
options(digits = 15)
dim_square = 2 ### set dimension of square matrix
# Generate random vector, random matrix, and
set.seed(1234)
A = matrix(nrow = dim_square, ncol = dim_square, byrow = T, rlnorm(dim_square ^ 2)/10)
b = rep(rnorm(1), dim_square)
# having fixed A & B, select X randomly
x = rnorm(dim_square) # vector length of dim_square--supposed to be arbitrary
f = function(x, A, b){
total_vector = A %*% x + b # this is the function that we want to minimize
total = 0.5 * sum(abs(total_vector) ^ 2) # L2 norm squared
return(total)
}
f(x,A,b)
# how close do we want to get?
epsilon = 0.1
delta = 0.01
value = (t(A) %*% A) %*% x - t(A) %*% b
L2_norm = (sum(abs(value) ^ 2)) ^ 0.5
steps = vector()
while(L2_norm > delta){
x = x - epsilon * value
value = (t(A) %*% A) %*% x - t(A) %*% b
L2_norm = (sum(abs(value) ^ 2)) ^ 0.5
print(L2_norm)
}
minimum = f(x, A, b)
minimum
minimum_minus = f(x - 0.5*epsilon, A, b)
minimum_minus # less than the minimum found by gradient descent! Why?
On page 94 of the pdf appearing at https://www.deeplearningbook.org/contents/numerical.html
I am trying to find the values of the vector x such that f(x) is minimized. However, as demonstrated by the minimum in my code, and minimum_minus, minimum is not the actual minimum, as it exceeds minimum minus.
Any idea what the problem might be?
Original Problem
Finding the value of x such that the quantity Ax - b is minimized is equivalent to finding the value of x such that Ax - b = 0, or x = (A^-1)*b. This is because the L2 norm is the euclidean norm, more commonly known as the distance formula. By definition, distance cannot be negative, making its minimum identically zero.
This algorithm, as implemented, actually comes quite close to estimating x. However, because of recursive subtraction and rounding one quickly runs into the problem of underflow, resulting in massive oscillation, below:
Value of L2 Norm as a function of step size
Above algorithm vs. solve function in R
Above we have the results of A %% x followed by A %% min_x, with x estimated by the implemented algorithm and min_x estimated by the solve function in R.
The problem of underflow, well known to those familiar with numerical analysis, is probably best tackled by the programmers of lower-level libraries best equipped to tackle it.
To summarize, the algorithm appears to work as implemented. Important to note, however, is that not every function will have a minimum (think of a straight line), and also be aware that this algorithm should only be able to find a local, as opposed to a global minimum.

Constrained optimization of a vector

I have a (non-symmetric) probability matrix, and an observed vector of integer outcomes. I would like to find a vector that maximises the probability of the outcomes, given the transition matrix. Simply, I am trying to estimate a distribution of particles at sea given their ultimate distribution on land, and a matrix of probabilities of a particle released from a given point in the ocean ending up at a given point on the land.
The vector that I want to find is subject to the constraint that all components must be between 0-1, and the sum of the components must equal 1. I am trying to figure out the best optimisation approach for the problem.
My transition matrix and data set are quite large, but I have created a smaller one here:
I used a simulated known at- sea distribution of
msim<-c(.3,.2,.1,.3,.1,0) and a simulated probability matrix (t) to come up with an estimated coastal matrix (Datasim2), as follows:
t<-matrix (c(0,.1,.1,.1,.1,.2,0,.1,0,0,.3,0,0,0,0,.4,.1,.3,0,.1,0,.1,.4,0,0,0,.1,0,.1,.1),
nrow=5,ncol=6, byrow=T)
rownames(t)<-c("C1","C2","C3","C4","C5") ### locations on land
colnames(t)<-c("S1","S2","S3","S4","S5","S6") ### locations at sea
Datasim<-as.numeric (round((t %*% msim)*500))
Datasim2<-c(rep("C1",95), rep("C2",35), rep("C3",90),rep("C4",15),rep("C5",30))
M <-c(0.1,0.1,0.1,0.1,0.1,0.1) ## starting M
I started with a straightforward function as follows:
EstimateSource3<-function(M,Data,T){
EstEndProbsall<-M%*%T
TotalLkhd<-rep(NA, times=dim(Data)[1])
for (j in 1:dim(Data)[1]){
ObsEstEndLkhd<-0
ObsEstEndLkhd<-1-EstEndProbsall[1,] ## likelihood of particle NOT ending up at locations other than the location of interest
IndexC<-which(colnames(EstEndProbsall)==Data$LocationCode[j], arr.ind=T) ## likelihood of ending up at location of interest
ObsEstEndLkhd[IndexC]<-EstEndProbsall[IndexC]
#Total likelihood
TotalLkhd[j]<-sum(log(ObsEstEndLkhd))
}
SumTotalLkhd<-sum(TotalLkhd)
return(SumTotalLkhd)
}
DistributionEstimate <- optim(par = M, fn = EstimateSource3, Data = Datasim2, T=t,
control = list(fnscale = -1, trace=5, maxit=500), lower = 0, upper = 1)
To constrain the sum to 1, I tried using a few of the suggestions posted here:How to set parameters' sum to 1 in constrained optimization
e.g. adding M<-M/sum(M) or SumTotalLkhd<-SumTotalLkhd-(10*pwr) to the body of the function, but neither yielded anything like msim, and in fact, the 2nd solution came up with the error “L-BFGS-B needs finite values of 'fn'”
I thought perhaps the quadprog package might be of some help, but I don’t think I have a symmetric positive definite matrix…
Thanks in advance for your help!
What about that: Let D = distribution at land, M = at sea, T the transition matrix. You know D, T, you want to calculate M. You have
D' = M' T
hence D' T' = M' (T T')
and accordingly D'T'(T T')^(-1) = M'
Basically you solve it as when doing linear regression (seems SO does not support math notation: ' is transpose, ^(-1) is ordinary matrix inverse.)
Alternatively, D may be counts of particles, and now you can ask questions like: what is the most likely distribution of particles at sea. That needs a different approach though.
Well, I have never done such models but think along the following lines. Let M be of length 3 and D of length 2, and T is hence 3x2. We know T and we observe D_1 particles at location 1 and D_2 particles at location 2.
What is the likelihood that you observe one particle at location D_1? It is Pr(D = 1) = M_1 T_11 + M_2 T_21 + M_3 T_32. Analogously, Pr(D = 2) = M_1 T_12 + M_2 T_22 + M_3 T_32. Now you can easily write the log-likelihood of observing D_1 and D_2 particles at locations 1 and 2. The code might look like this:
loglik <- function(M) {
if(M[1] < 0 | M[1] > 1)
return(NA)
if(M[2] < 0 | M[2] > 1)
return(NA)
M3 <- 1 - M[1] - M[2]
if(M3 < 0 | M3 > 1)
return(NA)
D[1]*log(T[1,1]*M[1] + T[2,1]*M[2] + T[3,1]*M3) +
D[2]*log(T[1,2]*M[1] + T[2,2]*M[2] + T[3,2]*M3)
}
T <- matrix(c(0.1,0.2,0.3,0.9,0.8,0.7), 3, 2)
D <- c(100,200)
library(maxLik)
m <- maxLik(loglik, start=c(0.4,0.4), method="BFGS")
summary(m)
I get the answer (0, 0.2, 0.8) when I estimate it but standard errors are very large.
As I told, I have never done it so I don't know it it makes sense.

Pointwise multiplication and right matrix division

I'm currently trying to recreate this Matlab function in R:
function X = uniform_sphere_points(n,d)
% X = uniform_sphere_points(n,d)
%
%function generates n points unformly within the unit sphere in d dimensions
z= randn(n,d);
r1 = sqrt(sum(z.^2,2));
X=z./repmat(r1,1,d);
r=rand(n,1).^(1/d);
X = X.*repmat(r,1,d);
Regarding the the right matrix division I installed the pracma package. My R code right now is:
uniform_sphere_points <- function(n,d){
# function generates n points uniformly within the unit sphere in d dimensions
z = rnorm(n, d)
r1 = sqrt(sum(z^2,2))
X = mrdivide(z, repmat(r1,1,d))
r = rnorm(1)^(1/d)
X = X * matrix(r,1,d)
return(X)
}
But it is not really working since I always end with a non-conformable arrays error in R.
This operation for sampling n random points from the d-dimensional unit sphere could be stated in words as:
Construct a n x d matrix with entries drawn from the standard normal distribution
Normalize each row so it has (2-norm) magnitude 1
For each row, compute a random value by taking a draw from the uniform distribution (between 0 and 1) and raise that value to the 1/d power. Multiply all elements in the row by that value.
The following R code does these operations:
unif.samp <- function(n, d) {
z <- matrix(rnorm(n*d), nrow=n, ncol=d)
z * (runif(n)^(1/d) / sqrt(rowSums(z^2)))
}
Note that in the second line of code I have taken advantage of the fact that multiplying a n x d matrix in R by a vector of length n will multiply each row by the corresponding value in that vector. This saves us the work of using repmat to construct matrices of exactly the same size as our original matrix for these sorts of row-specific operations.

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