I'd like to solve the following two coupled differential equations numerically:
d/dt Phi_i = 1 - 1/N * \sum_{j=1}^N( k_{ij} sin(Phi_i - Phi_j + a)
d/dt k_{ij} = - epsilon * (sin(Phi_i - Phi_j + b) + k_{ij}
with defined starting conditions phi_0 (1-dim array with N entries) and k_0 (2-dim array with NxN entries)
I tried this: Using DifferentialEquations.js, build a matrix of initial starting conditions u0 = hcat(Phi_0, k_0) (2-dim array, Nx(N+1)), and somehow define that the first equation applies to to first column (in my code [:,1]) , and the second equation applies to the other columns (in my code [:,2:N+1]).
using Distributions
using DifferentialEquations
N = 100
phi0 = rand(N)*2*pi
k0 = rand(Uniform(-1,1), N,N)
function dynamics(du, u, p, t)
a = 0.3*pi
b = -0.53*pi
epsi = 0.01
du[:,1] .= 1 .- 1/N .* sum.([u[i,j+1] * sin(u[i,1] - u[j,1] + a) for i in 1:N, j in 1:N], dims=2)
du[:,2:N+1] .= .- epsi .* [sin(u[i,1] - u[j,1] + b) + u[i,j+1] for i in 1:N, j in 1:N]
end
u0 = hcat(phi0, k0)
tspan = (0.0, 200.0)
prob = ODEProblem(dynamics, u0, tspan)
sol = solve(prob)
Running this lines of code result in this error:
LoadError: DimensionMismatch ("cannot broadcast array to have fewer dimensions")in expression starting at line 47 (which is sol = solve(prob))
I'm new to Julia, and I'm not sure if im heading in the right direction with this. Please help me!
First of all, edit the first package, which is Distributions and not Distribution, it took me a while to find the error xD
The main problem is the .= in your first equation. When you do that, you don't just assign new values to an array, you're making a view. I cannot explain you exactly what is a view, but what I can tell you is that, when you this kind of assign, the left and right side must have the same type.
For example:
N = 100
u = rand(N,N+1)
du = rand(N,N+1)
julia> u[:,1] .= du[:,1]
100-element view(::Array{Float64,2}, :, 1) with eltype Float64:
0.2948248997313967
0.2152933893895821
0.09114453738716022
0.35018616658607926
0.7788869975259098
0.2833659299216609
0.9093344091412392
...
The result is a view and not a Vector. With this syntax, left and right sides must have same type, and that does not happen in your example. Note that the types of rand(5) and rand(5,1) are different in Julia: the first is an Array{Float64,1} and the other is Array{Float64,2}. In your code, d[:,1] is an Array{Float64,1} but 1 .- 1/N .* sum.([u[i,j+1] * sin(u[i,1] - u[j,1] + a) for i in 1:N, j in 1:N], dims=2) is an Array{Float64,2}, that's why it doesn't work. You have two choices, change the equal sign for:
du[:,1] = ...
Or:
du[:,1] .= 1 .- 1/N .* sum.([u[i,j+1] * sin(u[i,1] - u[j,1] + a) for i in 1:N, j in 1:N], dims=2)[:,1]
The first choice is just a basic assign, the second choice uses the view way and matches the types of both sides.
Related
TLDR: how to setup nditer when my algorithm needs different number of values for each operand, but I want broadcasting to be applied over the "other" axes.
I'm in the process of converting some algorithms to cython since I've got a lot of looping overhead in the implementation.
Originally I implemented the algorithms with support for broadcasting to allow various use-cases, and I would like to keep that.
The algorithms are quite involved, but the issue can be summarized by the following example code:
a = np.arange(5)
b = np.arange(11)
c = 0
for idx in range(len(a)):
c += a[idx] * b[2 * idx]
c += a[idx] * b[2 * idx + 1]
Broadcasting of this could be implemented along the first axis with the exact same code:
a = np.arange(5 * 7).reshape((5, 7, 1))
b = np.arange(11 * 6).reshape((11, 1, 6))
c = 0
for idx in range(a.shape[0]):
c += a[idx] * b[2 * idx]
c += a[idx] * b[2 * idx + 1]
or along the last axis with some slight modifications (not the same result, but that's not of importance here):
a = np.arange(5 * 7).reshape((7, 1, 5))
b = np.arange(11 * 6).reshape((1, 6, 11))
c = 0
for idx in range(a.shape[-1]):
c += a[..., idx] * b[..., 2 * idx]
c += a[..., idx] * b[..., 2 * idx + 1]
The actual algorithms I have need multiple nested for-loops for each "broadcastable unit", can involve more "inputs" (a and b here) and the "output" (c) can also be another array instead of a single value.
When the inner loops are moved over to cython broadcasting is no longer an option. It seems like nditer would be the way to go here, but I cannot figure out how to make it ignore the fact that one of the axes is not broadcastable. I expected that
a = np.arange(5 * 7).reshape((7, 1, 5))
b = np.arange(11 * 6).reshape((1, 6, 11))
it = np.nditer([a, b], flags=['external_loop'])
would allow me to loop over all axes except the one where I apply my custom algorithm, but that does not seem to be the case. Instead I'm met with a ValueError: operands could not be broadcast together with shapes (7,1,5) (1,6,11).
Ideally I would be able to loop as
for a_inner, b_inner, out_inner in it:
out_inner[...] = call_to_cythonized_algorithm(a_inner, b_inner)
where the shapes of the _inner variables match with what I need for the algorithm (5, 11, 0 in the examples above), or potentially with one extra dimension which I could loop over in the cython code.
I've tried a couple other flags as well, but I don't really know what I'm doing, and none of them give me an iterator that works.
Is this possible with the current API, or have I found a limitation of nditer?
Suppose I have a recursive procedure with a formal parameter p. This procedure
wraps the recursive call in a Θ(1) (deferred) operation
and executes a Θ(g(k)) operation before that call.
k is dependent upon the value of p. [1]
The procedure calls itself with the argument p/b where b is a constant (assume it terminates at some point in the range between 1 and 0).
Question 1.
If n is the value of the argument to p in the initial call to the procedure, what are the orders of growth of the space and the number of steps executed, in terms of n, for the process this procedure generates
if k = p? [2]
if k = f(p)? [3]
Footnotes
[1] i.e., upon the value of the argument passed into p.
[2] i.e., the size of the input to the nested operation is same as that for our procedure.
[3] i.e., the size of the input to the nested operation is some function of the input size of our procedure.
Sample procedure
(define (* a b)
(cond ((= b 0) 0)
((even? b) (double (* a (halve b))))
(else (+ a (* a (- b 1))))))
This procedure performs integer multiplication as repeated additions based on the rules
a * b = double (a * (b / 2)) if b is even
a * b = a + (a * (b - 1)) if b is odd
a * b = 0 if b is zero
Pseudo-code:
define *(a, b) as
{
if (b is 0) return 0
if (b is even) return double of *(a, halve (b))
else return a + *(a, b - 1)
}
Here
the formal parameter is b.
argument to the recursive call is b/2.
double x is a Θ(1) operation like return x + x.
halve k is Θ(g(k)) with k = b i.e., it is Θ(g(b)).
Question 2.
What will be the orders of growth, in terms of n, when *(a, n) is evaluated?
Before You Answer
Please note that the primary questions are the two parts of question 1.
Question 2 can be answered as the first part. For the second part, you can assume f(p) to be any function you like: log p, p/2, p^2 etc.
I saw someone has already answered question 2, so I'll answer question 1 only.
First thing is to notice is that the two parts of the question are equivalent. In the first question, k=p so we execute a Θ(g(p)) operation for some function g. In the second one, k=f(p) and we execute a Θ(g(f(p))) = Θ((g∘f)(p)). replace g from the first question by g∘f and the second question is solved.
Thus, let's consider the first case only, i.e. k=p. Denote the time complexity of the recursive procedure by T(n) and we have that:
T(n) = T(n/b) + g(n) [The free term should be multiplied by a constant c, but we can talk about complexity in "amount of c's" and the theta bound will obviously remain the same]
The solution of the recursive formula is T(n) = g(n) + g(n/b) + ... + g(n/b^i) + ... + g(1)
We cannot further simplify it unless given additional information about g. For example, if g is a polynomial, g(n) = n^k, we get that
T(n) = n^k * (1 + b^-k + b^-2k + b^-4k + ... + b^-log(n)*k) <= n^k * (1 + b^-1 + b^-2 + ....) <= n^k * c for a constant c, thus T(n) = Θ(n^k).
But, if g(n) = log_b(n), [from now on I ommit the base of the log] we get that T(n) = log(n) + log(n/b) + ... + log(n/(b^log_b(n))) = log(n^log(n) * 1/b^(1 + 2 + ... log(n))) = log(n)^2 - log(n)^2 / 2 - log(n) / 2 = Θ(log(n) ^ 2) = Θ(g(n)^2).
You can easily prove, using a similar proof to the one where g is a polynomial that when g = Ω(n), i.e., at least linear, then the complexity is g(n). But when g is sublinear the complexity may be well bigger than g(n), as g(n/b) may be much bigger then g(n) / b.
You need to apply the wort case analysis.
First,
you can approximate the solution by using powers of two:
If then clearly the algorithm takes: (where ).
If it is an odd number then after applying -1 you get an even number and you divide by 2, you can repeat this only times, and the number of steps is also , the case of b being an odd number is clearly the worst case and this gives you the answer.
(I think you need an additional base case for: b=1)
What I have tried already: d = |v||PQ|sin("Theta")
Now, I need to determine what theta is, so I set up a position on a makeshift graph, the graph I made was on the xy plane only as the z plane complicates things needlessly for finding theta. So, I ended up with an acute angle, and if the angle is acute, then I have to find theta which according to dot product facts is greater than 0.
I do not have access to theta, so I used the same princples from cross dots. u * v = |u||v|cos("theta") but in this case, u and v are PQ and v. A vector is a vector, right?
so now I have theta = acos((v*PQ)/(|v||PQ))
with that I get (4sqrt(10))/15 = 32.5125173162 in degrees, so the angle is 32.5125173162 degrees.
So, now that I have theta, I plug it into my distance formula |v||PQ|sin(32.5125173162)
3*sqrt(10)*sin(32.5125173162) = 5.0990195136
or for the sake of simplicity, 5.1
I however want to know if this question is correct.
If it is NOT correct, what can I do to correct it? At what points did I use incorrect information?
This is not a question with a definitive answer in the back of the book, its a question on the side of a page that said: "try this!"
There are a couple of problems with this question.
From the context it looks like you mean for both v and PQ to be vectors. The "distance" between two vectors is an awkward (not well defined) question because vectors are not position bound.
You are using the cross product formula and I have no idea why:
|AxB| = |A||B|Sin(theta)
I think what you are actually trying to do is calculate the distance between the terminal points of the vectors, (2, 1, 2) and (1, 0, 3). Just use the Pythagorean Theorem (extended to 3D) for this.
d = sqrt( (x1 - x2)^2 + (y1 - y2)^2 + (z1 - z2)^2 )
d = sqrt( (2 - 1)^2 + (1 - 2)^2 + (2 - 3)^2 )
d = sqrt( 1^2 + (-1)^2 + (-1)^2 )
d = sqrt(3)
Edit:
If what you need really is the magnitude of the cross product, |AxB| then just find the cross product (using the determinant) and then calculate the magnitude of the result. There is no need for the formula you were using.
I am trying to write the code for solving the extremely difficult differential equation:
x' = 1
with the finite element method.
As far as I understood, I can obtain the solution u as
with the basis functions phi_i(x), while I can obtain the u_i as the solution of the system of linear equations:
with the differential operator D (here only the first derivative). As a basis I am using the tent function:
def tent(l, r, x):
m = (l + r) / 2
if x >= l and x <= m:
return (x - l) / (m - l)
elif x < r and x > m:
return (r - x) / (r - m)
else:
return 0
def tent_half_down(l,r,x):
if x >= l and x <= r:
return (r - x) / (r - l)
else:
return 0
def tent_half_up(l,r,x):
if x >= l and x <= r:
return (x - l) / (r - l)
else:
return 0
def tent_prime(l, r, x):
m = (l + r) / 2
if x >= l and x <= m:
return 1 / (m - l)
elif x < r and x > m:
return 1 / (m - r)
else:
return 0
def tent_half_prime_down(l,r,x):
if x >= l and x <= r:
return - 1 / (r - l)
else:
return 0
def tent_half_prime_up(l, r, x):
if x >= l and x <= r:
return 1 / (r - l)
else:
return 0
def sources(x):
return 1
Discretizing my space:
n_vertex = 30
n_points = (n_vertex-1) * 40
space = (0,5)
x_space = np.linspace(space[0],space[1],n_points)
vertx_list = np.linspace(space[0],space[1], n_vertex)
tent_list = np.zeros((n_vertex, n_points))
tent_prime_list = np.zeros((n_vertex, n_points))
tent_list[0,:] = [tent_half_down(vertx_list[0],vertx_list[1],x) for x in x_space]
tent_list[-1,:] = [tent_half_up(vertx_list[-2],vertx_list[-1],x) for x in x_space]
tent_prime_list[0,:] = [tent_half_prime_down(vertx_list[0],vertx_list[1],x) for x in x_space]
tent_prime_list[-1,:] = [tent_half_prime_up(vertx_list[-2],vertx_list[-1],x) for x in x_space]
for i in range(1,n_vertex-1):
tent_list[i, :] = [tent(vertx_list[i-1],vertx_list[i+1],x) for x in x_space]
tent_prime_list[i, :] = [tent_prime(vertx_list[i-1],vertx_list[i+1],x) for x in x_space]
Calculating the system of linear equations:
b = np.zeros((n_vertex))
A = np.zeros((n_vertex,n_vertex))
for i in range(n_vertex):
b[i] = np.trapz(tent_list[i,:]*sources(x_space))
for j in range(n_vertex):
A[j, i] = np.trapz(tent_prime_list[j] * tent_list[i])
And then solving and reconstructing it
u = np.linalg.solve(A,b)
sol = tent_list.T.dot(u)
But it does not work, I am only getting some up and down pattern. What am I doing wrong?
First, a couple of comments on terminology and notation:
1) You are using the weak formulation, though you've done this implicitly. A formulation being "weak" has nothing to do with the order of derivatives involved. It is weak because you are not satisfying the differential equation exactly at every location. FE minimizes the weighted residual of the solution, integrated over the domain. The functions phi_j actually discretize the weighting function. The difference when you only have first-order derivatives is that you don't have to apply the Gauss divergence theorem (which simplifies to integration by parts for one dimension) to eliminate second-order derivatives. You can tell this wasn't done because phi_j is not differentiated in the LHS.
2) I would suggest not using "A" as the differential operator. You also use this symbol for the global system matrix, so your notation is inconsistent. People often use "D", since this fits better to the idea that it is used for differentiation.
Secondly, about your implementation:
3) You are using way more integration points than necessary. Your elements use linear interpolation functions, which means you only need one integration point located at the center of the element to evaluate the integral exactly. Look into the details of Gauss quadrature to see why. Also, you've specified the number of integration points as a multiple of the number of nodes. This should be done as a multiple of the number of elements instead (in your case, n_vertex-1), because the elements are the domains on which you're integrating.
4) You have built your system by simply removing the two end nodes from the formulation. This isn't the correct way to specify boundary conditions. I would suggesting building the full system first and using one of the typical methods for applying Dirichlet boundary conditions. Also, think about what constraining two nodes would imply for the differential equation you're trying to solve. What function exists that satisfies x' = 1, x(0) = 0, x(5) = 0? You have overconstrained the system by trying to apply 2 boundary conditions to a first-order differential equation.
Unfortunately, there isn't a small tweak that can be made to get the code to work, but I hope the comments above help you rethink your approach.
EDIT to address your changes:
1) Assuming the matrix A is addressed with A[row,col], then your indices are backwards. You should be integrating with A[i,j] = ...
2) A simple way to apply a constraint is to replace one row with the constraint desired. If you want x(0) = 0, for example, set A[0,j] = 0 for all j, then set A[0,0] = 1 and set b[0] = 0. This substitutes one of the equations with u_0 = 0. Do this after integrating.
I have solid object that is spinning with a torque W, and I want to calculate the force F applied on a certain point that's D units away from the center of the object. All these values are represented in Vector3 format (x, y, z)
I know until now that W = D x F, where x is the cross product, so by expanding this I get:
Wx = Dy*Fz - Dz*Fy
Wy = Dz*Fx - Dx*Fz
Wz = Dx*Fy - Dy*Fx
So I have this equation, and I need to find (Fx, Fy, Fz), and I'm thinking of using the Simplex method to solve it.
Since the F vector can also have negative values, I split each F variable into 2 (F = G-H), so the new equation looks like this:
Wx = Dy*Gz - Dy*Hz - Dz*Gy + Dz*Hy
Wy = Dz*Gx - Dz*Hx - Dx*Gz + Dx*Hz
Wz = Dx*Gy - Dx*Hy - Dy*Gx + Dy*Hx
Next, I define the simplex table (we need <= inequalities, so I duplicate each equation and multiply it by -1.
Also, I define the objective function as: minimize (Gx - Hx + Gy - Hy + Gz - Hz).
The table looks like this:
Gx Hx Gy Hy Gz Hz <= RHS
============================================================
0 0 -Dz Dz Dy -Dy <= Wx = Gx
0 0 Dz -Dz -Dy Dy <= -Wx = Hx
Dz -Dz 0 0 Dx -Dx <= Wy = Gy
-Dz Dz 0 0 -Dx Dx <= -Wy = Hy
-Dy Dy Dx -Dx 0 0 <= Wz = Gz
Dy -Dy -Dx Dx 0 0 <= -Wz = Hz
============================================================
1 -1 1 -1 1 -1 0 = Z
The problem is that when I run it through an online solver I get Unbounded solution.
Can anyone please point me to what I'm doing wrong ?
Thanks in advance.
edit: I'm sure I messed up some signs somewhere (for example the Z should be defined as a max), but I'm sure I'm wrong when defining something more important.
There exists no unique solution to the problem as posed. You can only solve for the tangential projection of the force. This comes from the properties of the vector (cross) product - it is zero for collinear vectors and in particular for the vector product of a vector by itself. Therefore, if F is a solution of W = r x F, then F' = F + kr is also a solution for any k:
r x F' = r x (F + kr) = r x F + k (r x r) = r x F
since the r x r term is zero by the definition of vector product. Therefore, there is not a single solution but rather a whole linear space of vectors that are solutions.
If you restrict the solution to forces that have zero projection in the direction of r, then you could simply take the vector product of W and r:
W x r = (r x F) x r = -[r x (r x F)] = -[(r . F)r - (r . r)F] = |r|2F
with the first term of the expansion being zero because the projection of F onto r is zero (the dot denotes scalar (inner) product). Therefore:
F = (W x r) / |r|2
If you are also given the magnitude of F, i.e. |F|, then you can compute the radial component (if any) but there are still two possible solutions with radial components in opposing directions.
Quick dirty derivation...
Given D and F, you get W perpendicular to them. That's what a cross product does.
But you have W and D and need to find F. This is a bad assumption, but let's assume F was perpendicular to D. Call it Fp, since it's not necessarily the same as F. Ignoring magnitudes, WxD should give you the direction of Fp.
This ignoring magnitudes, so fix that with a little arithmetic. Starting with W=DxF applied to Fp:
mag(W) = mag(D)*mag(Fp) (ignoring geometry; using Fp perp to D)
mag(Fp) = mag(W)/mag(D)
Combining the cross product bit for direction with this stuff for magnitude,
Fp = WxD / mag(WxD) * mag(Fp)
Fp = WxD /mag(W) /mag(D) *mag(W) /mag(D)
= WxD / mag(D)^2.
Note that given any solution Fp to W=DxF, you can add any vector proportional to D to Fp to obtain another solution F. That is a totally free parameter to choose as you like.
Note also that if the torque applies to some sort of axle or object constrained to rotate about some axis, and F is applied to some oddball lever sticking out at a funny angle, then vector D points in some funny direction. You want to replace D with just the part perpendicular to the axle/axis, otherwise the "/mag(D)" part will be wrong.
So from your comment is clear that all rotations are spinning around center of gravity
in that case
F=M/r
F force [N]
M torque [N/m]
r scalar distance between center of rotation [m]
this way you know the scalar size of your Force
now you need the direction
it is perpendicular to rotation axis
and it is the tangent of the rotation in that point
dir=r x axis
F = F * dir / |dir|
bolds are vectors rest is scalar
x is cross product
dir is force direction
axis is rotation axis direction
now just change the direction according to rotation direction (signum of actual omega)
also depending on your coordinate system setup
so ether negate F or not
but this is in 3D free rotation very unprobable scenario
the object had to by symmetrical from mass point of view
or initial driving forces was applied in manner to achieve this
also beware that after first hit with any interaction Force this will not be true !!!
so if you want just to compute Force it generate on certain point if collision occurs is this fine
but immediately after this your spinning will change
and for non symmetric objects the spinning will be most likely off the center of gravity !!!
if your object will be disintegrated then you do not need to worry
if not then you have to apply rotation and movement dynamics
Rotation Dynamics
M=alpha*I
M torque [N/m]
alpha angular acceleration
I quadratic mass inertia for actual rotation axis [kg.m^2]
epislon''=omega'=alpha
' means derivation by time
omega angular speed
epsilon angle