I'm trying to implement the axioms for geometric products of two vectors in n-dimensioned Euclidean vector space.
i.e Allowing the user to input the number of dimensions, for e.g 3
Generate e1,e2,e3 as symbols
Allow the user to input two vectors as functions of n-sized vectors eg a=a1e1+a2e2+a3e3; b=b1e1+b2e2+b3e3
where a1,a2... and b1,b2... are scalars
Expand the two vectors a and b
(a1e1+a2e2+a3e3)(b1e1+b2e2+b3e3)
It all works fine for me until this point.
From this point, I require the expansion operation to be non commutative, i.e e2e1=/=e1e2
Is there a way I can do this.
Following that, if the reader is feeling kind, is there a way to implement geometric algebra axioms such as e1e1=e2e2=enen=1
and e2e1=-e1e2
-> eiej=-ejei
Thanks a lot!
What you'd like is a "multiplication" that enumerates all the pairs (T1, T2) where T1 is a term from the first expression, and T2 is a term from the second term.
function C = pairwise_mul(S1, S2)
% ' S1, S2 are cell arrays '
N1 = numel(S1);
N2 = numel(S2);
C = cell(1, N1*N2);
for k1 = 1:N1
for k2 = 1:N2
k = k2 + N1*(k1-1);
C{k} = [S1{k1} S2{k2}];
end;
end;
end
You can invoke it like this:
expr1 = {'a1', 'b1', 'c1'};
expr2 = {'a2', 'b2' };
result = pairwise_mul(expr1, expr2);
Related
This is a continuation of an earlier question I asked here.
If I create a symbolic expression in MatLab
syms L M T
F = M*L/T^2
I want to identify the powers of each dimension M, L, or T. In this case, the answer should be
for M, 1
for L, 1
for T, -2
There is a relatively easy way to do this if the expression F were a polynomial in MatLab employing the coeffs function. However, my expression is clearly not a polynomial as far as MatLab is concerned.
In the end, I will be working with at least two parameters so I will put them in a cell array since I anticipate cellfun will be useful.
V = L/T
param = {F,V};
The final output should be a table where the rows correspond to each dimension, L M and T and the columns are for each parameter F and V.
syms L M T
F = M*L/T^2
[C,T] = coeffs(expand(log(F),'IgnoreAnalyticConstraints',true))
[exp(T).' C.']
It returns the table:
Suppose I have a column vector of formulae like this
N =
4*k2 + 5*k3 + k1*x
7*k2 + 8*k3 + k1*y
and a column vector of symbolic variables like this
k =
k1
k2
k3
The formulae are linear with respect to k. I'd like to find a matrix M such that M*k equals N.
I can do this with N/k. However, that gives
[ (4*k2 + 5*k3 + k1*x)/k1, 0, 0]
[ (7*k2 + 8*k3 + k1*y)/k1, 0, 0]
which is correct, but not what I want. What I want is the matrix
x 4 5
y 7 8
which seems to me the simplest answer in that it involves no variables from k.
How do I convince Matlab to factor out the specified variables from a formula or a vector of formulae?
You can use coeffs, specifically the form
C = coeffs(p,vars) returns coefficients of the multivariate polynomial p with respect to the variables vars.
Since the first input needs to be a polynomial, you need to pass each component of N:
coeffs(N(1), k)
coeffs(N(2), k)
Or use a loop and store all results in a symbolic array:
result = sym('result', [numel(N) numel(k)]); % create symbolic array
for m = 1:numel(N)
result(m,:) = coeffs(N(m), k);
end
In your example, this gives
result =
[ 5, 4, x]
[ 8, 7, y]
Based on #LuisMendo's answer, I used coeffs. But there are a couple of problems with coeffs. The first is that its result doesn't include any coefficients that are 0. The second is that it doesn't seem to guarantee that the coefficients are ordered the same way as the variables in its second argument. I came up with the following function to replace coeffs.
Luckily coeffs returns a second result that lists the variables associated with each item in the first result. (It's more complicated if the formula is not linear.)
function m = factorFormula(f, v )
% Pre: f is a 1x1 sym representing a
% linear function of the variables in v.
% Pre: v is a column vector of variables
% Post: m is a row vector such that m*v equals f
% and the formulas in m do not contain the
% variables in v
[cx,tx] = coeffs(f,v)
n = size(v,1)
m = sym(zeros(1,n))
for i = 1:n
j = find(tx==v(i))
if size(j,2) == 1
m(i) = cx(j)
end
end
end
This only works for one formula, but it can be extended to a vector using the loop in #LuisMendo's answer or this equivalent expression in #Sanchises comment there.
cell2sym(arrayfun( #(f)factorFormula(f,k),N,'UniformOutput',false ) )
I hope there is a better answer than this.
Given an integer m, a hash function defined on T is a map T -> {0, 1, 2, ..., m - 1}. If k is an element of T and m is a positive integer, we denote hash(k, m) its hashed value.
For simplicity, most hash functions are of the form hash(k, m) = f(k) % m where f is a map from T to the set of integers.
In the case where m = 2^p (which is often used to the modulo m operation is cheap) and T is a set of integers, I have seen many people using f(k) = c * k with c being a prime number.
I understand if you want to choose a function of the form f(k) = c * k, you need to have gcd(c, m) = 1 for every hash table size m. Even though using a prime number fits the bill, c = 1 is also good.
So my question is the following: why do people still use f(k) = prime * k as their hash function? What kind of nice property does it have?
You don't need it to be prime. One of the most efficient hash functions with provable collision resistance just multiplies with a random number: https://en.wikipedia.org/wiki/Universal_hashing#Avoiding_modular_arithmetic. You do however need it to be odd.
I need to numerically evaluate some integrals which are all of the form shown in this image:
These integrals are the matrix elements of a N x N matrix, so I need to evaluate them for all possible combinations of n and m in the range of 1 to N. The integrals are symmetric in n and m which I have implemented in my current nested for loop approach:
function [V] = coulomb3(N, l, R, R0, c, x)
r1 = 0.01:x:R;
r2 = R:x:R0;
r = [r1 r2];
rl1 = r1.^(2*l);
rl2 = r2.^(2*l);
sines = zeros(N, length(r));
V = zeros(N, N);
for i = 1:N;
sines(i, :) = sin(i*pi*r/R0);
end
x1 = length(r1);
x2 = length(r);
for nn = 1:N
for mm = 1:nn
f1 = (1/6)*rl1.*r1.^2.*sines(nn, 1:x1).*sines(mm, 1:x1);
f2 = ((R^2/2)*rl2 - (R^3/3)*rl2.*r2.^(-1)).*sines(nn, x1+1:x2).*sines(mm, x1+1:x2);
value = 4*pi*c*x*trapz([f1 f2]);
V(nn, mm) = value;
V(mm, nn) = value;
end
end
I figured that calling sin(x) in the loop was a bad idea, so I calculate all the needed values and store them. To evaluate the integrals I used trapz, but as the first and the second/third integrals have different ranges the function values need to be calculated separately and then combined.
I've tried a couple different ways of vectorization but the only one that gives the correct results takes much longer than the above loop (used gmultiply but the arrays created are enourmous). I've also made an analytical solution (which is possible assuming m and n are integers and R0 > R > 0) but these solutions involve a cosine integral (cosint in MATLAB) function which is extremely slow for large N.
I'm not sure the entire thing can be vectorized without creating very large arrays, but the inner loop at least should be possible. Any ideas would be be greatly appreciated!
The inputs I use currently are:
R0 = 1000;
R = 8.4691;
c = 0.393*10^(-2);
x = 0.01;
l = 0 # Can reasonably be 0-6;
N = 20; # Increasing the value will give the same results,
# but I would like to be able to do at least N = 600;
Using these values
V(1, 1:3) = 873,379900963549 -5,80688363271849 -3,38139152472590
Although the diagonal values never converge with increasing R0 so they are less interesting.
You will lose the gain from the symmetricity of the problem with my approach, but this means a factor of 2 loss. Odds are that you'll still benefit in the end.
The idea is to use multidimensional arrays, making use of trapz supporting these inputs. I'll demonstrate the first term in your figure, as the two others should be done similarly, and the point is the technique:
r1 = 0.01:x:R;
r2 = R:x:R0;
r = [r1 r2].';
rl1 = r1.'.^(2*l);
rl2 = r2.'.^(2*l);
sines = zeros(length(r),N); %// CHANGED!!
%// V = zeros(N, N); not needed now, see later
%// you can define sines in a vectorized way as well:
sines = sin(r*(1:N)*pi/R0); %//' now size [Nr, N] !
%// note that implicitly r is of size [Nr, 1, 1]
%// and sines is of size [Nr, N, 1]
sines2mat = permute(sines,[1, 3, 2]); %// size [Nr, 1, N]
%// the first term in V: perform integral along first dimension
%//V1 = 1/6*squeeze(trapz(bsxfun(#times,bsxfun(#times,r.^(2*l+2),sines),sines2mat),1))*x; %// 4*pi*c prefactor might be physics, not math
V1 = 1/6*permute(trapz(bsxfun(#times,bsxfun(#times,r.^(2*l+2),sines),sines2mat),1),[2,3,1])*x; %// 4*pi*c prefactor might be physics, not math
The key point is that bsxfun(#times,r.^(2*l+2),sines) is a matrix of size [Nr,N,1], which is again multiplied by sines2mat using bsxfun, the result is of size [Nr,N,N] and an element (k1,k2,k3) corresponds to an integrand at radial point k1, n=k2 and m=k3. Using trapz() with explicitly the first dimension (which would be default) reduces this to an array of size [1,N,N], which is just what you need after a good squeeze(). Update: as per #Dev-iL's comment you should use permute instead of squeeze to get rid of the leading singleton dimension, as that might be more efficent.
The two other terms can be handled the same way, and of course it might still help if you restructure the integrals based on overlapping and non-overlapping parts.
I have a scenario in MATLAB where I would like to evaluate a function for certain parameter values. The parameters are extracted from an arbitrary number of arrays, and each array can have an arbitrary number of elements. I know the number of arrays and the number of elements in them before I call the function.
For example, say I have arrays A = [a1 a2 ... aL], B = [b1 b2 ... bM] and C = [c1 c2 ... cN].
for i = 1:length(A)
for j = 1:length(B)
for k = 1:length(C)
myfunc(A(i), B(j), C(k))
end
end
end
I am considering taking the L elements of A, M elements of B and N elements of C, and flatenning them into a cell array, and iterating with a single for loop over this cell array.
I was wondering if there was a MATLAB function that does something like this ... The result does not have to be a cell array. I want a way to avoid having multiple nested for loops. It is fine for a small number of loops, but as this number grows, it is pretty difficult to read and maintain.
ndgrid can be used to flatten several nested loops into one. It generates all combinations of values (variables aa, bb, cc in the code below), so that a single index (k below) can be used to traverse all combinations. But note that generating all combinations may required a lot of memory, depending on your L, M, N.
[cc, bb, aa] = ndgrid(C, B, A); %// outermost variable should be rightmost here
for k = 1:numel(aa)
myfunc(aa(k), bb(k), cc(k));
end
Thanks to direction from the accepted answer, I made a function that generalizes to any number of arrays. The result is a 2D array of N-tuples - where N is the number of input arrays.
function [Result] = makeTuples(varargin)
nInputArgs = length(varargin);
b = cell(1, nInputArgs);
a = flip(varargin);
[b{:}] = ndgrid(a{:});
bb = flip(b);
nOutputs = length(bb);
N = numel(bb{1});
Result = zeros(N, nInputArgs);
arr = zeros(1,nInputArgs);
for j = 1:N
for k = 1:nOutputs
arr(k) = bb{k}(j);
end
Result(j,:) = arr;
arr = zeros(1,nInputArgs);
end
end