The human race is going to town in a writhing, sweating affirmation of their own humanity. Neo and Trinity love each other so much that they sneak away from the orgy to fuck the ever loving shit out of each other, the camera lingering on the strands of saliva connecting their lips. Its also important because it does establish how strongly Neo and Trinity feel about each other and why Neo would choose to rescue her and risk the fate of humanity by saving her instead of going to the source.
It rules and is an important landmark for horny cinema.
#include<stdio.h>
#include<stdlib.h>
int i,j;
int **readMatrix(int rows,int cols){
int **a;
a=(int**)malloc(rows*sizeof(int*));
for(i=0;i<rows;i++){
a[i]=(int*)malloc(cols*sizeof(int));
}
for(i=0;i<rows;i++){
for(j=0;j<cols;j++){
scanf("%d",&a[i][j]);
}
}
return a;
}
void displayMatrix(int **a,int rows,int cols){
for(i=0;i<rows;i++){
for(j=0;j<cols;j++){
printf("%d ",a[i][j]);
}
printf("\n");
}
}
int main(){
int n,**a;
scanf("%d",&n);
a=readMatrix(n,n);
displayMatrix(a,n,n);
for(i=0;i<n;i++){
free(a[i]);
}
free(a);
return 0;
}
Happy new year everyone! I am selling things I don't need.
| Built Matrix ME SE | After trying a couple of gasket mount keyboards, I decided they are not for me. Plus the angle is too steep for me. I always go back to my Mode 80. Fully built with aluminum plate, 10/10 vint blacks filmed and lubed with 205g0 with 65g springs, lubed durock stabs. Comes with all foams and a stainless steel back plate. | $750 |
|---|---|---|
| POM mr suit plate | Unused, in the original bag. | SOLD |
| IBM Model M, Part number 51g8572 | Bolt modded. Will outlast you with a little care. Tested and working. Totally clean. Manufactured 13 December 1994. | $120 |
| IBM Model M, Part number 1390120 | Bolt modded. The rare square metal badge variant with no lock lights, from an era where such indicators were displayed on a terminal screen. Totally clean. Manufactured 18 Feb 1986. | $150 |
| Deskmats! very big. | Los Pollos Hermanos, and Redux. Like new. Los Pollos will come in its original box. | $20 each |
Open to offers, and international shipping is OK. Thank you!
For example:
| 35 -7 |
| 5 -1 |
This matrix cannot be inverted because its determinant is equals to 0. What does this mean to the matrix? What properties will it hold? Is it something special?
Hi, I need to write a matrix with where some of its elements are different square roots (square root of 2, the inverse of the square root of two, two divided by the square root of two... etc.). How can I write this matrix in rpn? If possible without saving the different values in variables as it would be a waste of memory and time for me.
Thanks for your time.
EDIT: forgot to say that it is the HP 50g
EDIT2: Solution in the comment section.
https://imgur.com/ICxNLte
I don't know the answer myself. An explanation of how you reached the answer would be appreciated.
I'm a bit confused about the "extra 50 people" and "40 people less" part. Idk whether to divide it evenly in the matrix or put 50 on the top and 0 on the bottom, same with Qbii
Hello,
I'm applying least squares with prior to a problem and I'd like to plot its covariance matrix.
However, I don't find resources that explore how to define this matrix.
If anyone could help me or provide me with a resource that would be awesome.
https://preview.redd.it/l109f560h4e71.png?width=1059&format=png&auto=webp&s=0d465f031de3a6e022c513933c3fcb4a18fb7d53
Thank you
Hi, does any one know the varying codes one can use to test if a matrix is a square on R besides ’is.square.matric’. Thank you.
Well as the title asks, Would you be able to run Dijkstra on lets say a [20][40] matrix?
The main reason I'm asking is because every example i see of Dijkstra, they have a perfectly square Adjacency matrix.
Thanks for any help!
I have a problem similar in structure to finding a column vector x that minimizes ||Ax||^2 , where ||.|| is the Frobenius norm. Here A is full column rank and is a very tall matrix.
This is a generic problem, and if you e.g. follow the proof given in the 2nd most upvoted response here by Rodrigo de Azevedo, it is easy to prove that the solution is the eigenvector of A^T A, corresponding to the smallest eigenvalue of A^T A.
My problem is somewhat similar to this one. With a matrix X = [x_1 , ..., x_N], and a collection of matrices {A_1, ... , A_N } all of the same dimensions, find the matrix X that minimize the sum, from n=1 to N, of || A_n x_n ||^2 , on the constraint that X X^T = I .
A couple questions here:
-
does anyone know what this type of problem is called? Is it right to call it "coupled least squares", since this optimization couples the optimization for the different x_n?
-
does anyone have any suggestions on how to find the solution to this problem?
Note: In this context, a square root of a matrix A is some matrix B such that A = B^2. Square root in this context DOES NOT mean A = B^{T}*B.
I wish that I could say I even knew where to begin with this one. My first attempt was to show that the matrix -I has no real square roots, but that failed to produce any rigorous results. While I was certainly able to show that all diagonalizable square roots of -I are complex solutions, I can't say with certainty that some defective matrix C exists that could disprove my assertion.
My professor mentioned something about Jordan forms stating: given a Jordan form matrix A, is there a B with B^2=A. If we let B=MJM^-1 then A=B^2=MJ^2M^-1. So A=J^2 since A is of Jordan form and would be similar to itself.
What I don't understand is how this means we need only consider "Jordan matrices J with the eigenvalues we want and to check whether each of the various possibilities J^2 could have A as the Jordan form."
I just feel so in over my head with this problem and don't even understand the argument trying to be made. Any help would be greatly appreciated.
I have a matrix of 3*36 how can I invert the first 9 elements than the next 9 and so on? Many thanks to anyone who is able to help.
