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I'm stuck on optimising an algorithm; the algorithm works as such. Say we have an expression a+b+c+d and the algorithm was to attain every combination of numbers possible up to a number; let's say, n= 10.
So the algorithm would be outputting this;
1+1+1+1
1+1+1+2
1+1+1+3
..............
1+1+1+10
1+1+2+1
And so on, until it reaches the end which would be
10+10+10+10
However the problem I'm having is the runtime of the algorithm increases rapidly as n(max number) increases.
Are there any ways of cutting the runtime when n increases. I'm seriously stumped.
Can't post source code ATM as I'm not at home, but it was done in c++.
For the combination part, if "a+b+c+d" equals "a+b+d+c", then you can save some time by write the loop as
for (int i = 0; i < n; ++i) {
for (int j = i; j < n; ++j {
// etc.
}
}
but O(n^4) is needed.
Otherwise, if the problem is exactly "attain every combination of numbers possible up to a number", there should be a closed form solution from 4 to 4*n.
you don't need to attain every combination. just calc the result. Or I misunderstanding the question.
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Is matrix multiplication speed faster for sparse matrixes than dense matrixes?
To give a simplified example,
does
"[[0,0],[0,0]] multiplies [[1,1],[1,1]]"
faster than
"[[256,256],[256,256]] multiplies [[1,1],[1,1]]" ?
The machine code algorithm to do a multiplication goes like this:
int mul(int a,int b)
{
int result = 0;
bit sign = sign(a) ^ sign(b);
a = abs(a); b = abs(b);
while (b != 0)
{
b = b>>1; // shift b right, bit0 into carry
if (carrySet()) result += a;
a = a<<1; // shift a left
// note: checks for overflow being left out
}
return (sign==0 ? sum : -sum);
}
You'll easily see that the more bits are set in the right operand, the more computations are necessary to sum up the left operand.
So, provided that your matrix multiplication boils down to machine code multiplications like this, a sparse matrix will multiply significantly faster than a dense matrix.
The question I cannot answer here is if the FPU will do this in a more efficient manner. You'll want to read some specs here. But even if a FPU (or GPU) is doing some sort of tweaking, I doubt the basic multiplication grinding loop looks very much different (interested in comments about this.)
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I'm wondering if anyone can propose an alternate way to write this code that avoids looping? Thanks!
for j=1:Ncol-1
for i=nr:Nitr
mydataD(k,1) = j;
mydataD(k,2) = i;
mydataD(k,3) = D(j,i);
k=k+1;
end
nr=nr+1;
end
[w,T] = kruskal(mydataD);
[r,c]=find(tril(D.',-1));
mydataD=[c,r,D(sub2ind(size(D),c,r))];
The transpose and swapped row/column are so that the row numbers are sorted. This doesn't include elements whose values are zero. If you actually want the zero values we'll have to change the first line:
[r,c]=find(tril(ones(size(D),-1));
mydataD=[c,r,D(sub2ind(size(D),c,r))];
This looks to me to be an attempted replacement for sparse matrices, changing from an adjacency matrix to an adjacency list. The equivalent sparse matrix would be:
mydataD=sparse(triu(D,1));
To avoid the loop you could do something like this:
mydataD = zeros(Ncol -1, 3);
i = linspace(1, Ncol -1, Ncol -1);
j = linspace(nr,Niter, Ncol -1);
mydataD(:,1) = i;
mydataD(:,2) = j;
mydataD(:,3) = D(j,i);
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i want to generate 4 unique digit each time from 1 to 12. For example: [1,3,4,6] or [1,3,10,6] or [1,2,3,4]... but not like [1,2,8,1] or [2,2,4,5]
because in last two chunk 1 comes twice and so as 2 in second chunk. Again i need to generate those unique chunk of numbers randomly.
Any help will be appreciated. Big thanks in advance...:)
In javascript:
var numbers = [0,1,2,3,4,5,6,7,8,9];
var uniqueNumbers = [];
function getRandom(){
return numbers[Math.floor(Math.random()*numbers.length)];
}
while (uniqueNumbers.length < 4){
var value = getRandom();
if(uniqueNumbers.indexOf(value) < 0)
uniqueNumbers.push(value);
}
console.log(uniqueNumbers);
In javascript
random: function (min,max,l){
var arr = [],m = [],n = 0;
if (max - min < l-1) return;
for (var i=0; i<=(max-min); i++)m[i] = i + min;
for (var i=0; i<l; i++) {n = Math.floor(Math.random()*(m.length)); arr[i]=m.splice(n,1);};
return arr;
}
Just use a random function and compare every generated random digit to the previous numbers.
You have not specify any programming language, but still try these steps whatever language you are going to use:
Generate a random number from 1 to 12
Generate second random number and check if it is same like first or not, if not than go ahead for third number and check it with first and second.
Repeat this process for all four digits
Implement above steps whatever programming language you want to use.
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I am trying to have this loop but it says its not possible. How can I create these kinds of loops?
for i in range (sposA,sposB) and for j in range(eposB+1,sposB,-1):
if tempstr[i] == ctempstr[j]:
pcount += 1
In basically any language, such a construct would be ambiguous. Are you trying to loop in two dimensions (if each index list is x and y long, are you doing x*y things total?) or in parallel (are the indices the same length, e.g. x, and paired, so you only do x things?).
If you want to loop in two dimensions, you simply nest loops:
for i in range(x):
for j in range(y):
doStuff(i, j)
If they're parallel, you can either create some functional dependence between them so you can convert index i into index j, or combine the indicies:
for i in range(x):
j = f(i)
doStuff(i, j)
or
for i, j in zip(range(x), range(y)):
doStuff(i, j)
The above stuff is in Python-pseudocode, but the control structures are broadly applicable in any imperative language (C, Python, Java...).
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How to calculate the sum of (1+a%m+a^2%m……+a^n%m) where
m=k!, 1<=k<=12, n<=10^18. How to Calculate this sum.
Using computer and the time limit is 3 sec.
Sorry about my mistake
1+a+a^2+...+a^n = (1+a+a^2+...+a^n)*(1-a)/(1-a) =
= (1 - a^(n+1))/(1-a)
In other words, your expression can be computed as:
(1 - a^(n+1))/(1-a) % m
Or, in programmatic form,
fmod((1-pow(a,n+1))/(1-a), m)
sum = 0;
i = 0;
while(i <= n){
sum = sum + math.pow(a,i);
i++;
}
result = sum % m;