I've tried implementing Jacobi method for compressed sparse row format. But i couldnt obtain the output correctly. Below is the coding i tried. I'm trying with a 4 by 4 sparse matrix which is a tridiagonal matrix stored in compressed form before implementing Jacobi iterative method. Please help.
clear all;
close all;
clc;
H=4;
a=2;
b=-1;
c=-1;
A = diag(a*ones(1,H)) + diag(b*ones(1,H-1),1) + diag(c*ones(1,H-1),-1);%Matrix A
n = size(A,1); % no of rows
m = size(A,2); % no of columns
V = [];
C = [];
R = [];
counter=1;
R= [counter];
for i=1:n
for j=1:m
if (A(i,j) ~= 0)
V = [V A(i,j)];
C = [C j];
counter=counter+1;
end
R(i+1)=counter;
end
end
b = [9,18,24,3];
x_new = [1 ; 1 ; 1 ; 1];
eps = 1e-5; % 1 x 10^(-10).
error = 1000; % use any large value greater than eps to make sure that the loop can work
counter2=1;
while (error > eps)
x_old = x_new;
for i=1:length(R)-1 %modified
t = 0;
for j=R(i):R(i+1)-1 %modified
if (C(j)~=i) %not equal
t = t + x_old(C(j))*A(i,C(j)); %modified
end
end
x_new(i,1) = (b(i) - t)/A(i,C(j)); % is a row vector
end
error = norm(x_new-x_old);
counter2=counter2+1;
end
x_new % print x
Expected output is
[28.1987 47.3978 48.5979 25.7986]
this is the coding i tried and the expected output is above. Thank you for your time and consideration.
I have written a function in matlab that applies the newton method to a given system of equations and its jacobian. The x vector that approaches the intersection of the two functions given in the system of equations however is stored like this:
2.06733483062522 -0.615946742374531
2.25465524043858 -0.428626332561163
1.13788162420748 -1.54539994879227
1.32520203402085 -1.35807953897890
0.802454971005455 -1.88082660199429
0.989775380818824 -1.69350619218092
0.743382354827609 -1.93989921817214
0.930702764640978 -1.75257880835877
0.741429028102197 -1.94185254489755
0.928749437915565 -1.7545321350841
here
x1 = [2.06733~ ; 2.25465~]
and thus they are stored vertically but i want them horizontally underneath each other. Any help is appreciated
I've tried to take the values out in a for loop using even and odd entries but for a system of equations of higher order this wouldnt work.
% INPUT
% f root function (may be vector valued )
% df derivative of f (or function returning Jacobian matrix )
% x0 initial guess
% tol desired tolerance
% maxIt maximum number of iterations
%
% OUTPUT
% x approximate solution
% success true means converged according to error estimator
% errEst error estimate per iteration
% xHist ( optional ) array with intermediate solutions
function [x, success , errEst, xHist ] = newton (f, df, x0 , tol , maxIt )
errEst = []; xHist = [];
for k = 1:1:size(f)
iter = 0; err = inf; x = x0; success = false;
while err > 0 && iter < maxIt
Fun = f{k}(x);
Jac = df{k}(x);
delta = -Jac\Fun;
err = norm(delta);
x = x + delta;
errEst = [errEst; err];
xHist = [xHist; x];
iter = iter + 1;
end
end
if err < tol
success = true;
end
end
it is called like this:
x0 = 1/sqrt(2) * [2;1]; grad = [sqrt(2)/2;sqrt(2)]; n = grad/norm(grad); t = [n(2),-n(1)]; p = 0.5;
x0 = x0 + 2 * (n+t); %x0 = x0 - 2 * (n+t);
f = {#(x)(x(1)/2)^2 + x(2)^2 - 1, #(x)n .* (x - x0) - p - 0.5 .* (t .* (x - x0)) .^ 2};
df = {#(x)x(1)/2 + 2 * x(2), #(x)n - t .* (t .*(x - x0))};
[x, success , errEst , xHist ] = newton(f, df, x0 , 10^(-12) , 20);
An analytical solution for cubic bezier length
seems not to exist, but it does not mean that
coding a cheap solution does not exist. By cheap I mean something like in the range of 50-100 ns (or less).
Does someone know anything like that? Maybe in two categories:
1) less error like 1% but more slow code.
2) more error like 20% but faster?
I scanned through google a bit but it doesn't
find anything which looks like a nice solution. Only something like divide on N line segments
and sum the N sqrt - too slow for more precision,
and probably too inaccurate for 2 or 3 segments.
Is there anything better?
Another option is to estimate the arc length as the average between the chord and the control net. In practice:
Bezier bezier = Bezier (p0, p1, p2, p3);
chord = (p3-p0).Length;
cont_net = (p0 - p1).Length + (p2 - p1).Length + (p3 - p2).Length;
app_arc_length = (cont_net + chord) / 2;
You can then recursively split your spline segment into two segments and calculate the arc length up to convergence. I tested myself and it actually converges pretty fast. I got the idea from this forum.
Simplest algorithm: flatten the curve and tally euclidean distance. As long as you want an approximate arc length, this solution is fast and cheap. Given your curve's coordinate LUT—you're talking about speed, so I'm assuming you use those, and don't constantly recompute the coordinates—it's a simple for loop with a tally. In generic code, with a dist function that computes the euclidean distance between two points:
var arclength = 0,
last=LUT.length-1,
i;
for (i=0; i<last; i++) {
arclength += dist(LUT[i], LUT[i+1]);
}
Done. arclength is now the approximate arc length based on the maximum number of segments you can form in the curve based on your LUT. Need things faster with a larger potential error? Control the segment count.
var arclength = 0,
segCount = ...,
last=LUT.length-2,
step = last/segCount,
s, i;
for (s=0; s<=segCount; s++) {
i = (s*step/last)|0;
arclength += dist(LUT[i], LUT[i+1]);
}
This is pretty much the simplest possible algorithm that still generates values that come even close to the true arc length. For anything better, you're going to have to use more expensive numerical approaches (like the Legendre-Gauss quadrature technique).
If you want to know why, hit up the arc length section of "A Primer on Bézier Curves".
in my case a fast and valid approach is this. (Rewritten in c# for Unity3d)
public static float BezierSingleLength(Vector3[] points){
var p0 = points[0] - points[1];
var p1 = points[2] - points[1];
var p2 = new Vector3();
var p3 = points[3]-points[2];
var l0 = p0.magnitude;
var l1 = p1.magnitude;
var l3 = p3.magnitude;
if(l0 > 0) p0 /= l0;
if(l1 > 0) p1 /= l1;
if(l3 > 0) p3 /= l3;
p2 = -p1;
var a = Mathf.Abs(Vector3.Dot(p0,p1)) + Mathf.Abs(Vector3.Dot(p2,p3));
if(a > 1.98f || l0 + l1 + l3 < (4 - a)*8) return l0+l1+l3;
var bl = new Vector3[4];
var br = new Vector3[4];
bl[0] = points[0];
bl[1] = (points[0]+points[1]) * 0.5f;
var mid = (points[1]+points[2]) * 0.5f;
bl[2] = (bl[1]+mid) * 0.5f;
br[3] = points[3];
br[2] = (points[2]+points[3]) * 0.5f;
br[1] = (br[2]+mid) * 0.5f;
br[0] = (br[1]+bl[2]) * 0.5f;
bl[3] = br[0];
return BezierSingleLength(bl) + BezierSingleLength(br);
}
I worked out the closed form expression of length for a 3 point Bezier (below). I've not attempted to work out a closed form for 4+ points. This would most likely be difficult or complicated to represent and handle. However, a numerical approximation technique such as a Runge-Kutta integration algorithm (see my Q&A here for details) would work quite well by integrating using the arc length formula.
Here is some Java code for the arc length of a 3 point Bezier, with points a,b, and c.
v.x = 2*(b.x - a.x);
v.y = 2*(b.y - a.y);
w.x = c.x - 2*b.x + a.x;
w.y = c.y - 2*b.y + a.y;
uu = 4*(w.x*w.x + w.y*w.y);
if(uu < 0.00001)
{
return (float) Math.sqrt((c.x - a.x)*(c.x - a.x) + (c.y - a.y)*(c.y - a.y));
}
vv = 4*(v.x*w.x + v.y*w.y);
ww = v.x*v.x + v.y*v.y;
t1 = (float) (2*Math.sqrt(uu*(uu + vv + ww)));
t2 = 2*uu+vv;
t3 = vv*vv - 4*uu*ww;
t4 = (float) (2*Math.sqrt(uu*ww));
return (float) ((t1*t2 - t3*Math.log(t2+t1) -(vv*t4 - t3*Math.log(vv+t4))) / (8*Math.pow(uu, 1.5)));
public float FastArcLength()
{
float arcLength = 0.0f;
ArcLengthUtil(cp0.position, cp1.position, cp2.position, cp3.position, 5, ref arcLength);
return arcLength;
}
private void ArcLengthUtil(Vector3 A, Vector3 B, Vector3 C, Vector3 D, uint subdiv, ref float L)
{
if (subdiv > 0)
{
Vector3 a = A + (B - A) * 0.5f;
Vector3 b = B + (C - B) * 0.5f;
Vector3 c = C + (D - C) * 0.5f;
Vector3 d = a + (b - a) * 0.5f;
Vector3 e = b + (c - b) * 0.5f;
Vector3 f = d + (e - d) * 0.5f;
// left branch
ArcLengthUtil(A, a, d, f, subdiv - 1, ref L);
// right branch
ArcLengthUtil(f, e, c, D, subdiv - 1, ref L);
}
else
{
float controlNetLength = (B-A).magnitude + (C - B).magnitude + (D - C).magnitude;
float chordLength = (D - A).magnitude;
L += (chordLength + controlNetLength) / 2.0f;
}
}
first of first you should Understand the algorithm use in Bezier,
When i was coding a program by c# Which was full of graphic material I used beziers and many time I had to find a point cordinate in bezier , whic it seem imposisble in the first look. so the thing i do was to write Cubic bezier function in my costume math class which was in my project. so I will share the code with you first.
//--------------- My Costum Power Method ------------------\\
public static float FloatPowerX(float number, int power)
{
float temp = number;
for (int i = 0; i < power - 1; i++)
{
temp *= number;
}
return temp;
}
//--------------- Bezier Drawer Code Bellow ------------------\\
public static void CubicBezierDrawer(Graphics graphics, Pen pen, float[] startPointPixel, float[] firstControlPointPixel
, float[] secondControlPointPixel, float[] endPointPixel)
{
float[] px = new float[1111], py = new float[1111];
float[] x = new float[4] { startPointPixel[0], firstControlPointPixel[0], secondControlPointPixel[0], endPointPixel[0] };
float[] y = new float[4] { startPointPixel[1], firstControlPointPixel[1], secondControlPointPixel[1], endPointPixel[1] };
int i = 0;
for (float t = 0; t <= 1F; t += 0.001F)
{
px[i] = FloatPowerX((1F - t), 3) * x[0] + 3 * t * FloatPowerX((1F - t), 2) * x[1] + 3 * FloatPowerX(t, 2) * (1F - t) * x[2] + FloatPowerX(t, 3) * x[3];
py[i] = FloatPowerX((1F - t), 3) * y[0] + 3 * t * FloatPowerX((1F - t), 2) * y[1] + 3 * FloatPowerX(t, 2) * (1F - t) * y[2] + FloatPowerX(t, 3) * y[3];
graphics.DrawLine(pen, px[i - 1], py[i - 1], px[i], py[i]);
i++;
}
}
as you see above, this is the way a bezier Function work and it draw the same Bezier as Microsoft Bezier Function do( I've test it). you can make it even more accurate by incrementing array size and counter size or draw elipse instead of line& ... . All of them depend on you need and level of accuracy you need and ... .
Returning to main goal ,the Question is how to calc the lenght???
well The answer is we Have tons of point and each of them has an x coorinat and y coordinate which remember us a triangle shape & especially A RightTriabgle Shape. so if we have point p1 & p2 , we can calculate the distance of them as a RightTriangle Chord. as we remeber from our math class in school, in ABC Triangle of type RightTriangle, chord Lenght is -> Sqrt(Angle's FrontCostalLenght ^ 2 + Angle's SideCostalLeghth ^ 2);
and there is this relation betwen all points we calc the lenght betwen current point and the last point before current point(exmp p[i - 1] & p[i]) and store sum of them all in a variable. lets show it in code bellow
//--------------- My Costum Power Method ------------------\\
public static float FloatPower2(float number)
{
return number * number;
}
//--------------- My Bezier Lenght Calculator Method ------------------\\
public static float CubicBezierLenghtCalculator(float[] startPointPixel
, float[] firstControlPointPixel, float[] secondControlPointPixel, float[] endPointPixel)
{
float[] tmp = new float[2];
float lenght = 0;
float[] px = new float[1111], py = new float[1111];
float[] x = new float[4] { startPointPixel[0], firstControlPointPixel[0]
, secondControlPointPixel[0], endPointPixel[0] };
float[] y = new float[4] { startPointPixel[1], firstControlPointPixel[1]
, secondControlPointPixel[1], endPointPixel[1] };
int i = 0;
for (float t = 0; t <= 1.0; t += 0.001F)
{
px[i] = FloatPowerX((1.0F - t), 3) * x[0] + 3 * t * FloatPowerX((1.0F - t), 2) * x[1] + 3F * FloatPowerX(t, 2) * (1.0F - t) * x[2] + FloatPowerX(t, 3) * x[3];
py[i] = FloatPowerX((1.0F - t), 3) * y[0] + 3 * t * FloatPowerX((1.0F - t), 2) * y[1] + 3F * FloatPowerX(t, 2) * (1.0F - t) * y[2] + FloatPowerX(t, 3) * y[3];
if (i > 0)
{
tmp[0] = Math.Abs(px[i - 1] - px[i]);// calculating costal lenght
tmp[1] = Math.Abs(py[i - 1] - py[i]);// calculating costal lenght
lenght += (float)Math.Sqrt(FloatPower2(tmp[0]) + FloatPower2(tmp[1]));// calculating the lenght of current RightTriangle Chord & add it each time to variable
}
i++;
}
return lenght;
}
if you wish to have faster calculation just need to reduce px & py array lenght and loob count.
We also can decrease memory need by reducing px and py to array lenght to 1 or make a simple double variable but becuase of Conditional situation Happend which Increase Our Big O I didn't do that.
Hope it helped you so much. if have another question just ask.
With Best regards, Heydar - Islamic Republic of Iran.
I need to find the smallest number of steps it takes to get between two points in a grid. If you are positioned at the center, and you can only move in 8 directions to the integer points surrounding you, then what's the least number of steps to take to get to a destination point?
I have a solution for this, but it is massively ugly and I'm honestly a bit ashamed of it:
/**
* #details Each point in the graph is a linear combination of these vectors:
*
* [x] = a[0] + b[1] + c[1] + d[ 1]
* [y] [1] [1] [0] [-1]
*
* EQ1: c + b + d == x
* EQ2: a + b - d == y
*
* Any path can be simplified to involve at most two of these variables, so we
* can solve the linear equations above with the knowledge that at least two of
* a, b, c, and d are 0. The sum of the absolute value of the coefficients is
* the number of steps taken in the grid.
*/
unsigned min_distance(point_t start, point_t goal)
{
int a, b, c, d;
int swap, steps;
int x, y;
x = goal.x - start.x;
y = goal.y - start.y;
/* Possible simple shortcuts */
if (x == 0 || y == 0) {
steps = abs(x) + abs(y);
} else if (abs(x) == abs(y)) {
steps = abs(y);
} else {
b = x, a = y - b;
steps = abs(a) + abs(b);
c = x, a = y;
swap = abs(a) + abs(c);
if (steps > swap)
steps = swap;
d = x, a = y + d;
swap = abs(a) + abs(d);
if (steps > swap)
steps = swap;
b = y, c = x - b;
swap = abs(b) + abs(c);
if (steps > swap)
steps = swap;
b = (x + y) / 2, d = b - y;
swap = abs(b) + abs(d);
if ((x + y) % 2 == 0 && steps > swap)
steps = swap;
d = -y, c = x - d;
swap = abs(c) + abs(d);
if (steps > swap)
steps = swap;
}
return steps;
}
The comment at the top explains the actual algorithm: represent each valid step as a column vector in a matrix, then find the smallest solution to the resulting system of linear equations.
In this case I saw the best answer would use at most two variables, and solved the equation six times by setting different combinations of the variables to 0. That's too specific! I want to be able to change the rules about which steps are valid and still be able to find the min distance.
EDIT: I realize this is a very poor simple example of what I'm trying to do, because of how easy it is to simplify the problem in this case. The goal is to calculate the number of steps given arbitrary stepping rules. If it the allowed steps instead looked like this then I'd start with a different matrix ([0 2 3 4 x; 1 2 0 -4 y]) and find the least solution to a different system of equations (2b + 3c + 4d = x, a + 2b - 4d = y). I'm actually trying to write a procedure that can work with any set of vectors to find the minimum number of steps.
...Any advice or criticism?
Please, run the following reproducible code:
require(compiler)
a <- 1:80
n <- 1:140
fn <- cmpfun(function(a, n)
{
K <- ceiling(runif(n, min = 1, max = 80))
p <- length(K[K >= min(80, a + 5)]) / n
return(p)
})
It is sure the fn() function returns a number smaller than 1, due to the fact that it should return the frequency of the random numbers from K which are greater than a + 5; this can be verified with random integer inputs in fn():
for(i in seq(8, 80, 8))
{
for(j in seq(14, 140, 14))
{
print(fn(i,j))
}
}
Now I would like to get the cross-function of fn() applied to a and n arrays, and I thought outer() was the best solution:
The outer product of the arrays X and Y is the array A with dimension c(dim(X), dim(Y)) where element A[c(arrayindex.x, arrayindex.y)] = FUN(X[arrayindex.x], Y[arrayindex.y], ...).
I would expect to get output from 0 to 1 but
persp(x = a, y = n, z = outer(X = a, Y = n, FUN = fn),
ticktype = 'detailed', phi = 30, theta = 120)
does return this output, instead:
What I am missing in the use of outer()?
length(K[K >= min(80, a + 5)]) isn't what you think it is. outer needs a function, which is vectorized in both parameters. Your function is vectorized by chance, but not in the way you want.
Look at this:
set.seed(42)
n <- 1:5
x <- runif(n, min = 1, max = 80)
#[1] 73.26968 75.02896 23.60502 66.60536 51.69790
x/n
#[1] 73.269677 37.514479 7.868341 16.651341 10.339579
As #Roland said, the function you need to input to outer need to be vectorized first. One way of doing this:
fn_vec <- Vectorize(fn)
outer(a,n,fn_vec)