Does anyone know how to minimize a function containing an integral in MATLAB? The function looks like this:
L = Int(t=0,t=T)[(AR-x)dt], A is a system parameter and R and x are related through:
dR/dt = axRY - bR, where a and b are constants.
dY/dt = -xRY
I read somewhere that I can use fminbnd and quad in combination but I am not able to make it work. Any suggestions?
Perhaps you could give more details of your integral, e.g. where is the missing bracket in [AR-x)dt]? Is there any dependence of x on t, or can we integrate dR/dt = axR - bR to give R=C*exp((a*x-b)*t)? In any case, to answer your question on fminbnd and quad, you could set A,C,T,a,b,xmin and xmax (the last two are the range you want to look for the min over) and use:
[x fval] = fminbnd(#(x) quad(#(t)A*C*exp((a*x-b)*t)-x,0,T),xmin,xmax)
This finds x that minimizes the integral.
If i didn't get it wrong you are trying to minimize respect to t:
\int_0^t{(AR-x) dt}
well then you just need to find the zeros of:
AR-x
This is just math, not matlab ;)
Here's some manipulation of your equations that might help.
Combining the second and third equations you gave gives
dR/dt = -a*(dY/dt)-bR
Now if we solve for R on the righthand side and plug it into the first equation you gave we get
L = Int(t=0,t=T)[(-A/b*(dR/dt + a*dY/dt) - x)dt]
Now we can integrate the first term to get:
L = -A/b*[R(T) - R(0) + Y(T) - Y(0)] - Int(t=0,t=T)[(x)dt]
So now all that matters with regards to R and Y are the endpoints. In fact, you may as well define a new function, Z which equals Y + R. Then you get
L = -A/b*[Z(T) - Z(0)] - Int(t=0,t=T)[(x)dt]
This next part I'm not as confident in. The integral of x with respect to t will give some function which is evaluated at t = 0 and t = T. This function we will call X to give:
L = -A/b*[Z(T) - Z(0)] - X(T) + X(0)
This equation holds true for all T, so we can set T to t if we want to.
L = -A/b*[Z(t) - Z(0)] - X(t) + X(0)
Also, we can group a lot of the constants together and call them C to give
X(t) = -A/b*Z(t) + C
where
C = A/b*Z(0) + X(0) - L
So I'm not sure what else to do with this, but I've shown that the integral of x(t) is linearly related to Z(t) = R(t) + Y(t). It seems to me that there are many equations that solve this. Anyone else see where to go from here? Any problems with my math?
Related
I have a simple quadratic equation, but I need to find a way for R to solve for X or Y depending on the value I input for either. For example, my equation is
y = 232352x^2+2468776x+381622
I need to find a code that solves for y when x = 8000 and solve for x when y = 4000. Does such a code/function exist in R or do I have to do it manually?
The first part (solving for y when x=8000) is pretty straightforward.
You just type:
232352 * 8000^2 + 2468776 * 8000 + 381622
and R gives:
[1] 1.489028e+13
The second problem involves roots. The polyroot() function is what you're after. It takes in the coefficients of the equation as a vector, and returns the roots. So for your case:
polyroot(c(381622-4000,2468776,232352))
gives:
[1] -0.155227+0i -10.469928-0i
And then it's up to you to figure out which solution you want.
Remember in general if you want to solve y = Ax^2 + Bx + C for a particular value of y, you have to rearrange the equation to Ax^2 + Bx + (C-y) = 0.
Translated to R code this is:
coeff <- c(C-y,B,A)
polyroot(coeff)
Where you substitute A,B,C,y with the relevant numbers.
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In my code I have 4 points: Q, R ,S , T.
I know the following
Coordinates for R, T, and S;
That segment RT < RQ < RS;
I need to figure out the coordinates of Q.
I already know point Q can be found on the line segment TS. However I need to get the coordinates for Q and I need it to be a relatively efficient calculation.
I have several solutions for this problem but they are all so convoluted and long I know I must be doing something wrong. I feel certain there must a simple elegant way to solve this. The best solution would be one that minimizes the number of more intensive calculations but that also isn't ridiculously long.
Q is the intersecting point between a circle of radius d around R and the line TS, which leads to a quadratic equation with a number of parameters in the coefficients. I don't know if the following if “the best” solution (it may even be better to use a numerical solver in between), but it is completely worked out. Because I think it's more readable, I've changed your coordinate names to put T at (T1, T2), S at (S1, S2) and, to keep the formulas shorter, R at (0, 0) – just adjust S and T and the returned values accordingly.
tmp1 = S1^2 - S2*T2 - S1*T1 + S2^2;
tmp2 = sqrt(- S1^2*T2^2 + S1^2*d^2 + 2*S1*S2*T1*T2 - 2*S1*T1*d^2 -
S2^2*T1^2 + S2^2*d^2 - 2*S2*T2*d^2 + T1^2*d^2 + T2^2*d^2);
tmp3 = S1^2 - 2*S1*T1 + S2^2 - 2*S2*T2 + T1^1 + T2^2;
t = (tmp1 + tmp2)/tmp3;
if (0 > t || t > 1) {
// pick the other solution instead
t = (tmp1 - tmp2)/tmp3;
}
Q1 = S1+t*(T1-S1);
Q2 = S2+t*(T2-S2);
Obviously, I take no warranties that I made no typos etc. :-)
EDIT: Alternatively, you could also get a good approximation by some iterative method (say, Newton) to find a zero of dist(S+t*(T-S), R)-d, as a function of t in [0,1]. That would take nine seven multiplications and one division per Newton step, if I count correctly. Re-using the names from above, that would look something like this:
t = 0.5;
d2 = d^2;
S1T1 = S1 - T1;
S2T2 = S2 - T2;
do {
tS1T1 = S1 - t*S1T1;
tS2T2 = S2 - t*S2T2;
f = tS1T1*tS1T1 + tS2T2*tS2T2 - d2;
fp = 2*(S1T1*tS1T1 + S2T2*tS2T2);
t = t + f/fp;
} while (f > eps);
Set eps to control your required accuracy, but do not set it too low – computing f does involve a subtraction that will have serious cancellation problems near the solution.
Since there are two solutions Q on the (TS) line (with only one solution between T and S), any solution probably involves some choice of sign, or arccos(), etc.
Therefore, a good solution is probably to put Q on the (TS) line like so (with vectors implied):
(1) TQ(t) = t * TS
(where O is some origin). Requiring that Q be at a distance d from R gives a 2nd degree equation in t, which is easy to solve (again, vectors are implied):
d^2 = |RQ(t)|^2 = |RT + TQ(t)|^2
The coordinates of Q can then be obtained by putting a solution t0 into equation (1), via OQ(t0) = OT + TQ(t). The solution 0 <= t <= 1 must be chosen, so that Q lies between T and S.
Now, it may happen that the final formula has some simple interpretation in terms of trigonometric functions… Maybe you can tell us what value of t and what coordinates you find with this method and we can look for a simpler formula?
I have a specific question about reversing atan2, I will write my code example in PHP.
$radial = 1.12*PI();
$transformed = -atan2(cos($radial)*2, sin($radial)*1.5);
$backToRadial = ?
Is there a way to reverse the transformed value to the start radial, without knowing the start radial?
This is how code flow should be: $radial => transform($radial) => transformback(transform($radial)) => $radial.
I have searched on the web (incl. stack) but I couldn't find any correct code. Also looked on Wikipedia, but it was overwhelming. My question is more a algebra question I think ;).
Let me know what you think!
_
Answered by Jason S:
Answer (by radial range: -PI-PI):
$backToRadial = atan2(2*cos($transformed), -1.5*sin($transformed));
Answer (by radial range: 0-2PI):
$backToRadial = PI() + atan2(-(2*cos($transformed)), -(-1.5*sin($transformed)));
A simple answer that will work for principal angles (angles over the range θ = -π to +π) is as follows:
θ' = -atan2(2cos θ, 1.5sin θ)
θ = atan2(2cos θ', -1.5sin θ')
where the first equation is your forward transformation, and the second equation is one of many inverse transformations.
The reason for this is that what you're doing is equivalent to a reflection + scaling + unit-magnitude-normalization of the cartesian coordinate pair (x,y) = (r cos θ, r sin θ) for r =1, since atan2(y,x) = θ.
A specific transformation that will work is (x',y') = (1.5y, -2x).
θ' = atan2(y',x') = atan2(-2x, 1.5y) = atan2(-2Rcos θ, 1.5Rsin θ) = -atan2(2 cos θ, 1.5 sin θ),
with the last step true since atan2(ky,kx) = atan2(y,x) for any k > 0, and -atan2(y,x) = atan2(-y, x).
This can be reversed by solving for x and y, namely y = 1/1.5 * x' and x = -1/2 * y':
θ = atan2(y,x) = atan2(1/1.5 * x', -1/2 * y')
and we choose to multiply (x,y) by k = 3/R to leave the angle unchanged:
θ = atan2(2x'/R, -1.5y'/R) = atan2(2 cos θ', -1.5 sin θ')
Q.E.D.
edit: Jason points out, correctly, that your example angle 1.12π is not in the principal angle range -π to +π. You need to define the range of angles you wish to be able to handle, and it has to be a range of at most length 2π.
My answer can be adjusted accordingly, but it takes a bit of work to verify, and you would make it easier on yourself if you stuck to the -π to +π range, since you are using atan2() and its output is in this range.
If you want to use a modified version of atan2() that outputs angles in the 0-2π range, I'd recommend using
atan2b(y,x) = pi+atan2(-y,-x)
where atan2b now outputs between 0 and 2π, since the calculation atan2(-y,-x) differs from atan2(y,x) by an angle of π (mod 2π)
If you're going to take this approach, don't calculate -atan2b(y,x); instead calculate atan2b(-y,x), (equivalent mod 2π) so that the range of output angles is left unchanged.
First, atan2 is not the same as tan-1 or arctan as seen below from the Wiki article on atan2:
As you can see, you cannot map it back without some information regarding x and y. However, if x>0 is always true, then you just take use the inverse tangent function, etc.
You could use this representation, to compute the inverse function:
In your example, y = 2cos(r) and x = 1.5sin(r). Therefore, if you divide the above expression with y, you get it in the form of x/y which in your case is 4/3 cot(r).
If this representation is correct, some simple algebra gives you:
where r = radial and k = cot(transformed/2)
WolframAlpha gave a solution to this:
But depending on your resources, it's probably better to find the root of the function with a fixed value of k. E.g. if k = 1.35, then you need to solve:
Any decent solver (and hence the comment on the resources you have) such as MATLAB will solve this. WolframAlpha provided the following approximate real solution:
Here is the setup. No assumptions for the values I am using.
n=2; % dimension of vectors x and (square) matrix P
r=2; % number of x vectors and P matrices
x1 = [3;5]
x2 = [9;6]
x = cat(2,x1,x2)
P1 = [6,11;15,-1]
P2 = [2,21;-2,3]
P(:,1)=P1(:)
P(:,2)=P2(:)
modePr = [-.4;16]
TransPr=[5.9,0.1;20.2,-4.8]
pred_modePr = TransPr'*modePr
MixPr = TransPr.*(modePr*(pred_modePr.^(-1))')
x0 = x*MixPr
Then it was time to apply the following formula to get myP
, where μij is MixPr. I used this code to get it:
myP=zeros(n*n,r);
Ptables(:,:,1)=P1;
Ptables(:,:,2)=P2;
for j=1:r
for i = 1:r;
temp = MixPr(i,j)*(Ptables(:,:,i) + ...
(x(:,i)-x0(:,j))*(x(:,i)-x0(:,j))');
myP(:,j)= myP(:,j) + temp(:);
end
end
Some brilliant guy proposed this formula as another way to produce myP
for j=1:r
xk1=x(:,j); PP=xk1*xk1'; PP0(:,j)=PP(:);
xk1=x0(:,j); PP=xk1*xk1'; PP1(:,j)=PP(:);
end
myP = (P+PP0)*MixPr-PP1
I tried to formulate the equality between the two methods and seems to be this one. To make things easier, I skipped the summation of matrix P in both methods .
where the first part denotes the formula that I used, and the second comes from his code snippet. Do you think this is an obvious equality? If yes, ignore all the above and just try to explain why. I could only start from the LHS, and after some algebra I think I proved it equals to the RHS. However I can't see how did he (or she) think of it in the first place.
Using E for expectation, the one dimensional version of your formula is the familiar:
Variance(X) = E((X-E(X))^2) = E(X^2) - E(X)^2
While the second form might be easier programming, I'd worry about ending up with a negative (or, in the multidimensional case, non positive definite) answer by using it, due to rounding error.
I know the coordinates of A, B and C.. I also know of a vector V originating from C..
I know that the vector intersects A and B, I just don't know how to find i.
Can anyone explain the steps involved in solving this problem?
Thanks alot.
http://img34.imageshack.us/img34/941/triangleprob.png
If you know A and B, you know equation for the line AB, and you said you know V, so you can form the equation for Line V.... Well i is only point that satisfies both those equations.
Equation for Line AB:
(bx-ax)(Y-ay) = (by-ay)(X-ax)
If you knpow the direction (or slope = m) of the vector, and any point that lies on the vector, then the equation of the line for vector V is
Y = mX = b
where m is the slope or direction of the line, and b is the y coordinate where it crosses thevertical y=axis (where X = 0)
if you know a point on the line (i.e., C = (s, t) then you solve for b by:
t = ms + b ==> b = t - ms,
so equation becomes
Y = mX + t-ms
i = C+kV
Lets call N the normal to the line A,B so N = [-(B-A).y, (B-A).x]
Also, for any point on the line:
(P-A)*N = 0 -- substitute from line 1 above:
(C+kV-A)*N = 0
(kV+C-A)*N = 0
kV*N + (C-A)*N = 0
kV*N = (A-C)*N
k = [(A-C)*N]/V*N
Now that we have k, plug it into line 1 above to get i.
Here I'm using * to represent dot product so expanding to regular multiplication:
k = ((A.x-C.x)*-(B.y-A.y) + (A.y-C.y)*(B.x-A.x)) / (V.x*-(B.y-A.y) + V.x*(B.x-A.x))
I.x = C.x + k*V.x
I.y = C.y + k*V.y
Unless I screwed something up....
Simple algebra. The hard part is often just writing down the basic equations, but once written down, the rest is easy.
Can you define a line that emanates from the point C = [c_x,c_y], and points along the vector V = [v_x,v_y]? A nice way to represent such a line is to use a parametric representation. Thus,
V(t) = C + t*V
In terms of the vector elements, we have it as
V(t) = [c_x + t*v_x, c_y + t*v_y]
Look at how this works. When t = 0, we get the point C back, but for any other value of t, we get some other point on the line.
How about the line segment that passes through A and B? One way to solve this problem would be to define a second line parametrically in the same fashion. Then solve for a system of two equations in two unknowns to find the intersection.
An easier approach is to look at the normal vector to the line segment AB. That vector is given as
N = [b_y - a_y , a_x - b_x]/sqrt((b_x - a_x)^2 + (b_y - a_y)^2)
Note that N is defined here to have a unit norm.
So now, when do we know if a point happens to lie along the line that connects A and B? This is easy now. That will happen when the dot product defined below is exactly zero.
dot(N,V(t) - A) = 0
Expand this, and solve for the parameter t. We can write it down using dot products.
t = dot(N,A-C)/dot(N,V)
Or, if you prefer,
t = (N_x*(a_x - c_x) + N_y*(a_y - c_y)) / (N_x*v_x + N_y*v_y))
And once we have t, substitute into the expression above for V(t). Lets see all of this work in practice. I'll pick some points A,B,C and a vector V.
A = [7, 3]
B = [2, 5]
C = [1, 0]
V = [1, 1]
Our normal vector N, after normalization, will look something like
N = [0.371390676354104, 0.928476690885259]
The line parameter, t, is then
t = 3.85714285714286
And we find the point of intersection as
C + t*V = [4.85714285714286, 3.85714285714286]
If you plot the points on a piece of paper it should all fit together, and all in only a few simple expressions.