I am trying to install Julia on my MAC. I came up with the following error during installation:
Any idea is appreciated about what the problem can be.
Edit:
"test.jl is like this:
using JuMP
using Gurobi
m = Model(solver=GurobiSolver())
m = Model()
#variable(m, x ) # No bounds
#variable(m, x >= lb ) # Lower bound only (note: 'lb <= x' is not valid)
#variable(m, x <= ub ) # Upper bound only
#variable(m, lb <= x <= ub ) # Lower and upper bounds
#variable(m, x == fixedval ) # Fixed to a value (lb == ub)
#variable(m, x[1:M,1:N] >= 0 )
aff = AffExpr([x, z], [3.0, 4.0], 2.0) # 3x + 4z + 2
quad = QuadExpr([x,y],[x,z],[3.0,4.0],aff) # 3x^2 + 4yz + 3x + 4z + 2
#constraints(m, begin
x >= 1
y - w <= 2
sum_to_one[i=1:3], z[i] + y == 1
end)
#expression(m, shared, sum(i*x[i] for i=1:5))
#constraint(m, shared + y >= 5)
#constraint(m, shared + z <= 10)
expr = #expression(m, [i=1:3], i*sum(x[j] for j=1:3))
A few suggestions:
- use the full path in your code to be sure where the file is located
- Julia is case sensitive, so Test.jl and test.jl are different
- check the permissions on the file
- Julia makes heavy use of "test.jl" type names, potential confusion?
Related
My problem is as follows:
I'm given a natural number n and I want to find all natural numbers x and y such that
n = x² + y²
Since this is addition order does not matter so I count (x,y) and (y,x) as one solution.
My initial algorithm is to assume that y>x, for all x compute y²=n-x² and check if y is a natural number using binary search on y².
for(x=1;2*x*x<n;x++)
{
y_squared=n-x*x;
if(isSquare(y_squared)==false)
continue;
//rest of the code
}
Is there any improvement for my algorithm? I already checked if n can have solutions using two squares theorem, but I want to know how many there are.
My algorithm is O(sqrt(n) * log(n) )
Thanks in advance.
You can reduce this to O(sqrt(n)) this way:
all_solutions(n):
x = 0
y = floor(sqrt(n))
while x <= y
if x * x + y * y < n
x++
else if x * x + y * y > n
y--
else
// found a match
print(x, y)
x++
y--
This algorithm will find and print all possible solutions and will always terminate for x <= sqrt(n / 2) and y >= sqrt(n / 2), leading to at most sqrt(n / 2) + (sqrt(n) - sqrt(n / 2)) = sqrt(n) iterations being performed.
A variation of Paul's, keeping track of the sum of squares and adjusting it just with additions/subtractions:
Pseudo-code: (evaluate x++ + x and y-- + y left-to-right, or do it like in the Python code below)
x = 0
y = floor(sqrt(n))
sum = y * y
while x <= y
if sum < n
sum += x++ + x
else if sum > n
sum -= y-- + y
else
print(x, y)
sum += 2 * (++x - y--)
Java:
static void allSolutions(int n) {
int x = 0, y = (int) Math.sqrt(n), sum = y * y;
while (x <= y) {
if (sum < n) {
sum += x++ + x;
} else if (sum > n) {
sum -= y-- + y;
} else {
System.out.println(x + " " + y);
sum += 2 * (++x - y--);
}
}
}
Python:
from math import isqrt
def all_solutions(n):
x = 0
y = isqrt(n)
sum = y ** 2
while x <= y:
if sum < n:
x += 1
sum += 2 * x - 1
elif sum > n:
sum -= 2 * y - 1
y -= 1
else:
# found a match
print(x, y)
x += 1
sum += 2 * (x - y)
y -= 1
Demo:
>>> all_solutions(5525)
7 74
14 73
22 71
25 70
41 62
50 55
How can I prove Big Theta using quantificational definition? I know that u have to find 2 constants such that c1*g(n)<= f(n)<= c2*g(n)- but how do you find these constants?
Could anyone help me prove the following to show an example 5x3 − 7x2 + 5x + 1 = Θ(x3)?
Let's assume x > 0, which is usually what we have.
5x^3 − 7x^2 + 5x + 1 <= 5x^3 + 5x + 1
<= 5x^3 + 5x^3 + x^3 ; x >= 1
= 11x^3
On the other hand
5x^3 − 7x^2 + 5x + 1 >= 5x^3 - 7x^2
>= 5x^3 - 4x^3 ; if 7x^2 <= 4x3, i.e. x >= 7/4
= x^3
In conclusion, for x >= 7/4 we have:
x^3 <= (5x^3 − 7x^2 + 5x + 1) <= 11x^3
and we are done.
I have used Solve to find the solution of an equation in Mathematica (The reason I am posting here is that no one could answer my question in mathematica stack.)The solution is called s and it is a function of two variables called v and ro. I want to find imaginary and real part of s and I want to use the information that v and ro are real and they are in the below interval:
$ 0.02 < ro < 1 ,
40
The code I used is as below:
ClearAll["Global`*"]
d = 1; l = 100; k = 0.001; kk = 0.001;ke = 0.0014;dd = 0.5 ; dr = 0.06; dc = 1000; p = Sqrt[8 (ro l /2 - 1)]/l^2;
m = (4 dr + ke^2 (d + dd)/2) (-k^2 + kk^2) (1 - l ro/2) (d - dd)/4 -
I v p k l (4 dr + ke^2 (d + dd)/2)/4 - v^2 ke^2/4 + I v k dr l p/4;
xr = 0.06/n;
tr = d/n;
dp = (x (v I kk/2 (4 dr + ke^2 (d + dd)/2) - I v kk ke^2 (d - dd)/8 - dr l p k kk (d - dd)/4) + y ((xr I kk (ro - 1/l) (4 dr + ke^2 (d + dd)/2)) - I v kk tr ke^2 (1/l - ro/2) + I dr xr 4 kk (1/l - ro/2)))/m;
a = -I v k dp/4 - I xr y kk p/2 + l ke^2 dp p (d + dd)/8 + (-d + dd)/4 k kk x + dr l p dp;
aa = -v I kk dp/4 + xr I y k p/2 - tr y ke^2 (1/l - ro/2) - (d - dd) x kk^2/4 + ke^2 x (d - dd)/8;
ca = CoefficientArrays[{x (s + ke^2 (d + dd)/2) +
dp (v I kk - l (d - dd) k p kk/2) + y (tr ro ke^2) - (d -
dd) ((-kk^2 + k^2) aa - 2 k kk a)/(4 dr + ke^2 (d + dd)/2) == 0, y (s + dc ke^2) + n x == 0}, {x, y}];
mat = Normal[ca];
matt = Last#mat;
sha = Solve[Det[matt] == 0, s];
shaa = Assuming[v < 100 && v > 40 && ro < 1 && ro > 0.03,Simplify[%]];
reals = Re[shaa];
ims = Im[shaa];
Solve[reals == 0, ro]
but it gives no answer. Could anyone help? I really appreciate any solution to this problem.
I run your code down to this point
mat = Normal[ca]
and look at the result.
There are lots of very tiny floating point coefficients, so small that I suspect most of them are just floating point noise now. Mathematica thinks 0.1 is only known to 1 significant digit of precision and your mat result is perhaps nothing more than zero correct digits now.
I continue down to this point
sha = Solve[Det[matt] == 0, s]
If you look at the value of sha you will see it is s->stuff and I don't think that is at all what you think it is. Mathematica returns "rules" from Solve, not just expressions.
If I change that line to
sha = s/.Solve[Det[matt] == 0, s]
then I am guessing that is closer to what you are imagining you want.
I continue to
shaa = Assuming[40<v<100 && .03<ro<1, Simplify[sha]];
reals = Re[shaa]
And I instead use, because you are assuming v and ro to be Real and because ComplexExpand has often been very helpful in getting Re to provide desired results,
reals=Re[ComplexExpand[shaa]]
and I click on Show ALL to see the full expanded value of that. That is about 32 large screens full of your expression.
In that are hundreds of
Arg[-1. + 50. ro]
and if I understand your intention I believe all those simplify to 0. If that is correct then
reals=reals/.Arg[-1. + 50. ro]->0
reduces the size of reals down to about 20 large screen fulls.
But there are still hundreds of examples of Sqrt[(-1.+50. ro)^2] and ((-1.+50. ro)^2)^(1/4) making up your reals. Unfortunately I'm expecting your enormous expression is too large and will take too long for Simplify with assumptions to be able to be practically effective.
Perhaps additional replacements to coax it into dramatically simplifying your reals without making any mistakes about Real versus Complex, but you have to be extremely careful with such things because it is very common for users to make mistakes when dealing with complex numbers and roots and powers and functions and end up with an incorrect result, might get your problem down to the point where it might be feasible for
Solve[reals == 0, ro]
to give you a meaningful answer.
This should give you some ideas of what you need to think carefully about and work on.
I am building my first large-scale MATLAB program, and I've managed to write original vectorized code for everything so for until I came to trying to create an image representing vector density in stereographic projection. After a couple failed attempts I went to the Mathworks file exchange site and found an open source program which fits my needs courtesy of Malcolm Mclean. With a test matrix his function produces something like this:
And while this is almost exactly what I wanted, his code relies on a triply nested for-loop. On my workstation a test data matrix of size 25000x2 took 65 seconds in this section of code. This is unacceptable since I will be scaling up to a data matrices of size 500000x2 in my project.
So far I've been able to vectorize the innermost loop (which was the longest/worst loop), but I would like to continue and be rid of the loops entirely if possible. Here is Malcolm's original code that I need to vectorize:
dmap = zeros(height, width); % height, width: scalar with default value = 32
for ii = 0: height - 1 % 32 iterations of this loop
yi = limits(3) + ii * deltay + deltay/2; % limits(3) & deltay: scalars
for jj = 0 : width - 1 % 32 iterations of this loop
xi = limits(1) + jj * deltax + deltax/2; % limits(1) & deltax: scalars
dd = 0;
for kk = 1: length(x) % up to 500,000 iterations in this loop
dist2 = (x(kk) - xi)^2 + (y(kk) - yi)^2;
dd = dd + 1 / ( dist2 + fudge); % fudge is a scalar
end
dmap(ii+1,jj+1) = dd;
end
end
And here it is with the changes I've already made to the innermost loop (which was the biggest drain on efficiency). This cuts the time from 65 seconds down to 12 seconds on my machine for the same test matrix, which is better but still far slower than I would like.
dmap = zeros(height, width);
for ii = 0: height - 1
yi = limits(3) + ii * deltay + deltay/2;
for jj = 0 : width - 1
xi = limits(1) + jj * deltax + deltax/2;
dist2 = (x - xi) .^ 2 + (y - yi) .^ 2;
dmap(ii + 1, jj + 1) = sum(1 ./ (dist2 + fudge));
end
end
So my main question, are there any further changes I can make to optimize this code? Or even an alternative method to approach the problem? I've considered using C++ or F# instead of MATLAB for this section of the program, and I may do so if I cannot get to a reasonable efficiency level with the MATLAB code.
Please also note that at this point I don't have ANY additional toolboxes, if I did then I know this would be trivial (using hist3 from the statistics toolbox for example).
Mem consuming solution
yi = limits(3) + deltay * ( 1:height ) - .5 * deltay;
xi = limits(1) + deltax * ( 1:width ) - .5 * deltax;
dx = bsxfun( #minus, x(:), xi ) .^ 2;
dy = bsxfun( #minus, y(:), yi ) .^ 2;
dist2 = bsxfun( #plus, permute( dy, [2 3 1] ), permute( dx, [3 2 1] ) );
dmap = sum( 1./(dist2 + fudge ) , 3 );
EDIT
handling extremely large x and y by breaking the operation into blocks:
blockSize = 50000; % process up to XX elements at once
dmap = 0;
yi = limits(3) + deltay * ( 1:height ) - .5 * deltay;
xi = limits(1) + deltax * ( 1:width ) - .5 * deltax;
bi = 1;
while bi <= numel(x)
% take a block of x and y
bx = x( bi:min(end, bi + blockSize - 1) );
by = y( bi:min(end, bi + blockSize - 1) );
dx = bsxfun( #minus, bx(:), xi ) .^ 2;
dy = bsxfun( #minus, by(:), yi ) .^ 2;
dist2 = bsxfun( #plus, permute( dy, [2 3 1] ), permute( dx, [3 2 1] ) );
dmap = dmap + sum( 1./(dist2 + fudge ) , 3 );
bi = bi + blockSize;
end
This is a good example of why starting a loop from 1 matters. The only reason that ii and jj are initiated at 0 is to kill the ii * deltay and jj * deltax terms which however introduces sequentiality in the dmap indexing, preventing parallelization.
Now, by rewriting the loops you could use parfor() after opening a matlabpool:
dmap = zeros(height, width);
yi = limits(3) + deltay*(1:height) - .5*deltay;
matlabpool 8
parfor ii = 1: height
for jj = 1: width
xi = limits(1) + (jj-1) * deltax + deltax/2;
dist2 = (x - xi) .^ 2 + (y - yi(ii)) .^ 2;
dmap(ii, jj) = sum(1 ./ (dist2 + fudge));
end
end
matlabpool close
Keep in mind that opening and closing the pool has significant overhead (10 seconds on my Intel Core Duo T9300, vista 32 Matlab 2013a).
PS. I am not sure whether the inner loop instead of the outer one can be meaningfully parallelized. You can try to switch the parfor to the inner one and compare speeds (I would recommend going for the big matrix immediately since you are already running in 12 seconds and the overhead is almost as big).
Alternatively, this problem can be solved in using kernel density estimation techniques. This is part of the Statistics Toolbox, or there's this KDE implementation by Zdravko Botev (no toolboxes required).
For the example code below, I get 0.3 seconds for N = 500000, or 0.7 seconds for N = 1000000.
N = 500000;
data = [randn(N,2); rand(N,1)+3.5, randn(N,1);]; % 2 overlaid distrib
tic; [bandwidth,density,X,Y] = kde2d(data); toc;
imagesc(density);
I determine limits as limit(0)=0; limit(y)=2*1.08^(y-1), y∈{1,2,3,...,50} or if you prefeer iterative functions:
limit(0)=0
limit(1)=2
limit(y)=limit(y-1)*1.08, x∈{2,3,4,...,50}
Exmples:
limit(1) = 2*1.08^0 = 2
limit(2) = 2*1.08^1 = 2.16
limit(3) = 2*1.08^2 = 2.3328
...
for a given x∈[0,infinity) I want an efficient formula to calculate y so that limit(y)>x and limit(y-1)≤x or 50 if there is none.
Any ideas?
or is pre-calculating the 50 limits and using a couple of ifs the best solution?
I am using erlang as language, but I think it will not make much of a difference.
limit(y) = 2 * 1.08^(y-1)
limit(y) > x >= limit(y - 1)
Now if I haven't made a mistake,
2 * 1.08^(y - 1) > x >= 2 * 1.08^(y - 2)
1.08^(y - 1) > x / 2 >= 1.08^(y - 2)
y - 1 > log[1.08](x / 2) >= y - 2
y + 1 > 2 + ln(x / 2) / ln(1.08) >= y
y <= 2 + ln(x / 2) / ln(1.08) < y + 1
Which gives you
y = floor(2 + ln(x / 2) / ln(1.08))