MiniZinc does not find a solution to the scheduling problem - minizinc

I have a problem finding a solution using MiniZinc.
The task:
It is necessary to make a schedule of shifts for employees.
In one day there are three shifts: day (D), evening (E) and night (N).
It is necessary to draw up an optimal schedule, if possible avoiding undesirable situations:
Avoid single shifts (one shift between two breaks)
Avoid single breaks (shift, break, shift)
Avoid double breaks (shift, break, break, shift)
After a night shift should be a full day off (three breaks in a row)
To find a solution, I minimize the number of undesirable situations.
When I start the calculation, MiniZinc displays several intermediate variants, but does not find a final solution.
Is it possible to somehow optimize the calculations?
include "regular.mzn";
int: n = 21;
int: m = 6;
set of int: D = 1..n;
set of int: E = 1..m;
% Number of employees per shift
%|Sun |Mon |Tue |Wen |Thur |Fri |Sat |
array[D] of int: SHIFTS = [2, 2, 2, 2, 2, 1, 2, 2, 1, 2, 2, 1, 2, 2, 1, 2, 2, 1, 2, 2, 1];
/*2, 2, 1, 2, 2, 1, 2, 2, 1, 2, 2, 1, 2, 2, 1, 2, 2, 1, 2, 2, 1,
2, 2, 1, 2, 2, 1, 2, 2, 1, 2, 2, 1, 2, 2, 1, 2, 2, 1, 2, 2, 1,
2, 2, 1, 2, 2, 1, 2, 2, 1, 2, 2, 1, 2, 2, 1, 2, 2, 1, 2, 2, 2];*/
% The range of the number of shifts per employee for the period ([|from, to)
array[E, 1..2] of int: DC_SHIFTS = [|0, 10 %emp1
|0, 10 %emp2
|0, 10 %emp3
|0, 10 %emp4
|0, 10 %emp5
|0, 10 %emp6
|];
%-------------------------------------------------
% Variables
%-------------------------------------------------
array[E, D] of var 1..4: X;
% Counters of avoidable situations
var int: OS_PENALTY; % break, shift, break (single shift)
var int: NS_PENALTY; % night shift, not break, not break, not break (full day off after a night shift)
var int: DS_PENALTY; % shift, break, break, shift (two breaks between shifts)
var int: OO_PENALTY; % shift, break, shift (one break between shifts)
%-------------------------------------------------
% Constraints
%-------------------------------------------------
constraint
forall(d in D)(
sum(e in E)(bool2int(X[e, d] != 4)) = SHIFTS[d]
);
constraint
forall(e in E)(
sum(d in D)(bool2int(X[e, d] != 4)) >= DC_SHIFTS[e, 1]
/\
sum(d in D)(bool2int(X[e, d] != 4)) < DC_SHIFTS[e, 2]
);
constraint
forall(d in D)(
if d mod 3 = 1 then forall(e in E)(X[e, d] = 1 \/ X[e, d] = 4) else
if d mod 3 = 2 then forall(e in E)(X[e, d] = 2 \/ X[e, d] = 4) else
forall(e in E)(X[e, d] = 3 \/ X[e, d] = 4) endif endif
);
NS_PENALTY = sum(e in E, d in D where d < max(D) - 2)(bool2int(
X[e, d] = 3 \/ (X[e,d+1] != 4 /\ X[e,d + 2] != 4 /\ X[e,d + 3] != 4)
));
DS_PENALTY = sum(e in E, d in D where d < max(D) - 2)(bool2int(X[e, d] != 4 \/ X[e, d + 1] = 4 \/ X[e, d + 2] = 4 \/ X[e, d + 3] != 4));
OS_PENALTY = sum(e in E, d in D where d < max(D) - 1)(bool2int(X[e, d] = 4 /\ X[e, d + 1] != 4 /\ X[e, d + 2] = 4));
OO_PENALTY = sum(e in E, d in D where d < max(D) - 1)(bool2int(X[e, d] != 4 \/ X[e, d + 1] = 4 \/ X[e, d + 2] != 4));
%-------------------------------------------------
% Solve
%-------------------------------------------------
solve minimize OS_PENALTY + NS_PENALTY + DS_PENALTY + OO_PENALTY;
%-------------------------------------------------
% Output
%-------------------------------------------------
array[1..4] of string: rest_view = ["D", "E", "N", "-"];
output
[
rest_view[fix(X[e, d])] ++
if d = n then "\n" else "" endif
| e in E, d in D
];

I suggest the following changes to your model:
Change the declaration of X to array[E, D] of var 0..1: X; where 0 means break and 1 shift. Whether it's a day, evening or night shift is handled in the output section, where the results are transformed to show the shift type like if fix(X[e, d]) == 0 then "-" else rest_view[1 + (d-1) mod 3] endif.
Rewrite the constraints using globals like:
import "globals.mzn";
constraint
forall(d in D)(
exactly(SHIFTS[d], col(X, d), 1)
%sum(e in E)(bool2int(X[e, d] != 0)) = SHIFTS[d]
);
constraint
forall(e in E)(
global_cardinality_low_up(row(X, e), [1], [DC_SHIFTS[e, 1]], [DC_SHIFTS[e, 2] - 1])
%sum(d in D)(bool2int(X[e, d] != 0)) >= DC_SHIFTS[e, 1]
%/\
%sum(d in D)(bool2int(X[e, d] != 0)) < DC_SHIFTS[e, 2]
);
%constraint
% forall(d in D)(
% if d mod 3 = 1 then forall(e in E)(X[e, d] = 1 \/ X[e, d] = 4) else
% if d mod 3 = 2 then forall(e in E)(X[e, d] = 2 \/ X[e, d] = 4) else
% forall(e in E)(X[e, d] = 3 \/ X[e, d] = 4) endif endif
% );
Rewrite the penalties like:
NS_PENALTY = sum(e in E, d in 1..n - 3 where d mod 3 = 0)(bool2int(
X[e, d] = 1 /\ (sum(i in 1..3)(X[e,d+i]) > 0)
));
DS_PENALTY = sum(e in E, d in 1..n - 3)(bool2int(X[e, d] != 0 /\ X[e, d + 1] = 0 /\ X[e, d + 2] = 0 /\ X[e, d + 3] != 0));
OS_PENALTY = sum(e in E, d in 1..n - 2)(bool2int(X[e, d] = 0 /\ X[e, d + 1] != 0 /\ X[e, d + 2] = 0));
OO_PENALTY = sum(e in E, d in 1..n - 2)(bool2int(X[e, d] != 0 /\ X[e, d + 1] = 0 /\ X[e, d + 2] != 0));

Related

Element-wise matrix multiplication for multi-dimensional array

I want to realize component-wise matrix multiplication in MATLAB, which can be done using numpy.einsum in Python as below:
import numpy as np
M = 2
N = 4
I = 2000
J = 300
A = np.random.randn(M, M, I)
B = np.random.randn(M, M, N, J, I)
C = np.random.randn(M, J, I)
# using einsum
D = np.einsum('mki, klnji, lji -> mnji', A, B, C)
# naive for-loop
E = np.zeros(M, N, J, I)
for i in range(I):
for j in range(J):
for n in range(N):
E[:,n,j,i] = B[:,:,i] # A[:,:,n,j,i] # C[:,j,i]
print(np.sum(np.abs(D-E))) # expected small enough
So far I use for-loop of i, j, and n, but I don't want to, at least for-loop of n.
Option 1: Calling numpy from MATLAB
Assuming your system is set up according to the documentation, and you have the numpy package installed, you could do (in MATLAB):
np = py.importlib.import_module('numpy');
M = 2;
N = 4;
I = 2000;
J = 300;
A = matpy.mat2nparray( randn(M, M, I) );
B = matpy.mat2nparray( randn(M, M, N, J, I) );
C = matpy.mat2nparray( randn(M, J, I) );
D = matpy.nparray2mat( np.einsum('mki, klnji, lji -> mnji', A, B, C) );
Where matpy can be found here.
Option 2: Native MATLAB
Here the most important part is to get the permutations right, so we need to keep track of our dimensions. We'll be using the following order:
I(1) J(2) K(3) L(4) M(5) N(6)
Now, I'll explain how I got the correct permute order (let's take the example of A): einsum expects the dimension order to be mki, which according to our numbering is 5 3 1. This tells us that the 1st dimension of A needs to be the 5th, the 2nd needs to be 3rd and the 3rd needs to be 1st (in short 1->5, 2->3, 3->1). This also means that the "sourceless dimensions" (meaning those that have no original dimensions becoming them; in this case 2 4 6) should be singleton. Using ipermute this is really simple to write:
pA = ipermute(A, [5,3,1,2,4,6]);
In the above example, 1->5 means we write 5 first, and the same goes for the other two dimensions (yielding [5,3,1]). Then we just add the singletons (2,4,6) at the end to get [5,3,1,2,4,6]. Finally:
A = randn(M, M, I);
B = randn(M, M, N, J, I);
C = randn(M, J, I);
% Reference dim order: I(1) J(2) K(3) L(4) M(5) N(6)
pA = ipermute(A, [5,3,1,2,4,6]); % 1->5, 2->3, 3->1; 2nd, 4th & 6th are singletons
pB = ipermute(B, [3,4,6,2,1,5]); % 1->3, 2->4, 3->6, 4->2, 5->1; 5th is singleton
pC = ipermute(C, [4,2,1,3,5,6]); % 1->4, 2->2, 3->1; 3rd, 5th & 6th are singletons
pD = sum( ...
permute(pA .* pB .* pC, [5,6,2,1,3,4]), ... 1->5, 2->6, 3->2, 4->1; 3rd & 4th are singletons
[5,6]);
(see note regarding sum at the bottom of the post.)
Another way to do it in MATLAB, as mentioned by #AndrasDeak, is the following:
rD = squeeze(sum(reshape(A, [M, M, 1, 1, 1, I]) .* ...
reshape(B, [1, M, M, N, J, I]) .* ...
... % same as: reshape(B, [1, size(B)]) .* ...
... % same as: shiftdim(B,-1) .* ...
reshape(C, [1, 1, M, 1, J, I]), [2, 3]));
See also: squeeze, reshape, permute, ipermute, shiftdim.
Here's a full example that shows that tests whether these methods are equivalent:
function q55913093
M = 2;
N = 4;
I = 2000;
J = 300;
mA = randn(M, M, I);
mB = randn(M, M, N, J, I);
mC = randn(M, J, I);
%% Option 1 - using numpy:
np = py.importlib.import_module('numpy');
A = matpy.mat2nparray( mA );
B = matpy.mat2nparray( mB );
C = matpy.mat2nparray( mC );
D = matpy.nparray2mat( np.einsum('mki, klnji, lji -> mnji', A, B, C) );
%% Option 2 - native MATLAB:
%%% Reference dim order: I(1) J(2) K(3) L(4) M(5) N(6)
pA = ipermute(mA, [5,3,1,2,4,6]); % 1->5, 2->3, 3->1; 2nd, 4th & 6th are singletons
pB = ipermute(mB, [3,4,6,2,1,5]); % 1->3, 2->4, 3->6, 4->2, 5->1; 5th is singleton
pC = ipermute(mC, [4,2,1,3,5,6]); % 1->4, 2->2, 3->1; 3rd, 5th & 6th are singletons
pD = sum( permute( ...
pA .* pB .* pC, [5,6,2,1,3,4]), ... % 1->5, 2->6, 3->2, 4->1; 3rd & 4th are singletons
[5,6]);
rD = squeeze(sum(reshape(mA, [M, M, 1, 1, 1, I]) .* ...
reshape(mB, [1, M, M, N, J, I]) .* ...
reshape(mC, [1, 1, M, 1, J, I]), [2, 3]));
%% Comparisons:
sum(abs(pD-D), 'all')
isequal(pD,rD)
Running the above we get that the results are indeed equivalent:
>> q55913093
ans =
2.1816e-10
ans =
logical
1
Note that these two methods of calling sum were introduced in recent releases, so you might need to replace them if your MATLAB is relatively old:
S = sum(A,'all') % can be replaced by ` sum(A(:)) `
S = sum(A,vecdim) % can be replaced by ` sum( sum(A, dim1), dim2) `
As requested in the comments, here's a benchmark comparing the methods:
function t = q55913093_benchmark(M,N,I,J)
if nargin == 0
M = 2;
N = 4;
I = 2000;
J = 300;
end
% Define the arrays in MATLAB
mA = randn(M, M, I);
mB = randn(M, M, N, J, I);
mC = randn(M, J, I);
% Define the arrays in numpy
np = py.importlib.import_module('numpy');
pA = matpy.mat2nparray( mA );
pB = matpy.mat2nparray( mB );
pC = matpy.mat2nparray( mC );
% Test for equivalence
D = cat(5, M1(), M2(), M3());
assert( sum(abs(D(:,:,:,:,1) - D(:,:,:,:,2)), 'all') < 1E-8 );
assert( isequal (D(:,:,:,:,2), D(:,:,:,:,3)));
% Time
t = [ timeit(#M1,1), timeit(#M2,1), timeit(#M3,1)];
function out = M1()
out = matpy.nparray2mat( np.einsum('mki, klnji, lji -> mnji', pA, pB, pC) );
end
function out = M2()
out = permute( ...
sum( ...
ipermute(mA, [5,3,1,2,4,6]) .* ...
ipermute(mB, [3,4,6,2,1,5]) .* ...
ipermute(mC, [4,2,1,3,5,6]), [3,4]...
), [5,6,2,1,3,4]...
);
end
function out = M3()
out = squeeze(sum(reshape(mA, [M, M, 1, 1, 1, I]) .* ...
reshape(mB, [1, M, M, N, J, I]) .* ...
reshape(mC, [1, 1, M, 1, J, I]), [2, 3]));
end
end
On my system this results in:
>> q55913093_benchmark
ans =
1.3964 0.1864 0.2428
Which means that the 2nd method is preferable (at least for the default input sizes).

Gauss elimination to solve A*x = b linear system (MATLAB)

I'm trying to make a code that solves A*x = b, linear systems.
I made the code below using the gauss elimination process, and it works everytime if A doesn't have any 0's in it. If A has zeros in it, then sometimes it works, sometimes it doesn't. Basically I'm trying an alternative to the "A\b" in MATLAB.
Is there a better/simpler way of doing this?
A = randn(5,5);
b = randn(5,1);
nn = size(A);
n = nn(1,1);
U = A;
u = b;
for c = 1:1:n
k = U(:,c);
for r = n:-1:c
if k(r,1) == 0
continue;
else
U(r,:) = U(r,:)/k(r,1);
u(r,1) = u(r,1)/k(r,1);
end
end
for r = n:-1:(c+1)
if k(r,1) == 0
continue;
else
U(r,:) = U(r,:) - U(r-1,:);
u(r,1) = u(r,1) - u(r-1,1);
end
end
end
x = zeros(size(b));
for r = n:-1:1
if r == n
x(r,1) = u(r,1);
else
x(r,1) = u(r,1);
x(r,1) = x(r,1) - U(r,r+1:n)*x(r+1:n,1);
end
end
error = A*x - b;
for i = 1:1:n
if abs(error(i)) > 0.001
disp('ERROR!');
break;
else
continue;
end
end
disp('x:');
disp(x);
Working example with 0's:
A = [1, 3, 1, 3;
3, 4, 4, 1;
3, 0, 3, 9;
0, 4, 0, 1];
b = [3;
4;
5;
6];
Example that fails (A*x-b isn't [0])
A = [1, 3, 1, 3;
3, 4, 4, 1;
0, 0, 3, 9;
0, 4, 0, 1];
b = [3;
4;
5;
6];
Explanation of my algorithm:
Lets say I have the following A matrix:
|4, 1, 9|
|3, 4, 5|
|1, 3, 5|
For the first column, I divide each line by the first number in the row, so every row starts with 1
|1, 1/4, 9/4|
|1, 4/3, 5/3|
|1, 3, 5|
Then I subtract the last row with the one above it, and then I'll do the same for the row above and so on.
|1, 1/4, 9/4|
|0, 4/3-1/4, 5/3-9/4|
|0, 3-4/3, 5-5/3|
|1, 0.25, 2.250|
|0, 1.083, -0.5833|
|0, 1.667, 3.333|
Then I repeat the same for the rest of the columns.
|1, 0.25, 2.250|
|0, 1, -0.5385|
|0, 1, 1.999|
|1, 0.25, 2.250|
|0, 1, -0.5385|
|0, 0, -8.7700|
|1, 0.25, 2.250|
|0, 1, -0.5385|
|0, 0, 1|
The same operations I do in A I do in b so the system stays equivalent.
re-UPDATE:
I added this right after "for c = 1:1:n"
So before doing anything it sorts the rows of A (and b) in order to make the "c" column have decrescent entries (0's will be left on the bottom rows of A). Right now it seems to work for any invertible square matrix, although I'm not sure it will.
r = c;
a = r + 1;
while r <= n
if r == n
r = r + 1;
elseif a <= n
while a <= n
if abs(U(r,c)) < abs(U(a,c))
UU = U(r,:);
U(r,:) = U(a,:);
U(a,:) = UU;
uu = u(r,1);
u(r,1) = u(a,1);
u(a,1) = uu;
else
a = a+1;
end
end
else
r = r+1;
a = r+1;
end
end
Gaussian elimination with pivoting is as following.
function [L,U,P] = my_lu_piv(A)
n = size(A,1);
I = eye(n);
O = zeros(n);
L = I;
U = O;
P = I;
function change_rows(k,p)
x = P(k,:); P(k,:) = P(p,:); P(p,:) = x;
x = A(k,:); A(k,:) = A(p,:); A(p,:) = x;
x = v(k); v(k) = v(p); v(p) = x;
end
function change_L(k,p)
x = L(k,1:k-1); L(k,1:k-1) = L(p,1:k-1);
L(p,1:k-1) = x;
end
for k = 1:n
if k == 1, v(k:n) = A(k:n,k);
else
z = L(1:k-1,1:k -1)\ A(1:k-1,k);
U(1:k-1,k) = z;
v(k:n) = A(k:n,k)-L(k:n,1:k-1)*z;
end
if k<n
x = v(k:n); p = (k-1)+find(abs(x) == max(abs(x))); % find index p
change_rows(k,p);
L(k+1:n,k) = v(k+1:n)/v(k);
if k > 1, change_L(k,p); end
end
U(k,k) = v(k);
end
end
In order to solve the system..
% Ax = b (1) original system % LU = PA
(2) factorization of PA or A(p,:) into the product LU % PAx =
Pb (3) multiply both sides of (1) by P % LUx = Pb
(4) substitute (2) into (3) % let y = Ux (5) define y as
Ux % let c = Pb (6) define c as Pb % Ly = c
(7) subsitute (5) and (6) into (4) % U*x = y (8) a
rewrite of (5)
To do this..
% [L U p] = lu (A) ; % factorize % y = L \ (P*b) ; % forward
solve of (7), a lower triangular system % x = U \ y ; %
backsolve of (8), an upper triangular system
Gaussian algorithm assumes that the matrix is converted to an upper triangular matrix. This does not happen in your example. The result of your algorithm is
A =
1 3 1 3
3 4 4 1
0 0 3 9
0 4 0 1
U =
1.00000 3.00000 1.00000 3.00000
-0.00000 1.00000 -0.20000 1.60000
0.00000 0.00000 1.00000 3.00000
0.00000 4.00000 -0.00000 1.00000
As you can see, it's not upper triangular. You are skipping rows, if the pivot element is zero. That does not work. To fix this you need to swap columns in the matrix and rows in the vector if the pivot element is zero. At the end you have to swap back rows in your result b resp. u.
Gaussian algorithm is:
1 Set n = 1
2 Take pivot element (n, n)
3 If (n, n) == 0, swap column n with column m, so that m > n and (n, m) != 0 (swap row m and n in vector b)
4 Divide n-th row by pivot element (divide n-th row in vector b)
5 For each m > n
6 If (m, n) != 0
7 Divide row m by m and subtract element-wise row n (same for vector b)
8 n = n + 1
9 If n <= number of rows, go to line 2
In terms of numerical stability it would be best to use the maximum of each row as pivot element. Also you can use the maximum of the matrix as pivot element by swapping columns and rows. But remember to swap in b and to swap back in your solution.
Try this:
Ab = [A,b] % Extended matrix of the system of equations
rref(Ab) % Result of applying the Gauss-Jordan elimination to the extended matrix
See rref documentation for more details and examples.

how can i make my merge function show the possible combinations of lists in PROLOG

i am working on merging of lists in PROLOG. What are the possible changes i need to do to make my code show these outputs.
?- merge(X, [1,2], [1,2,3]). # lists that merge with [1,2] to give [1,2,3]
X = [1,2,3] ? ;
X = [1,3] ? ;
X = [2,3] ? ;
X = [3] ? ;
no
?- merge(X, Y, [1,2]). # pairs of lists that merge to give [1,2]
X = [], Y = [1,2] ? ;
X = [1,2], Y = [] ? ;
X = [1], Y = [1,2] ? ;
X = [1,2], Y = [1] ? ;
X = [1,2], Y = [1,2] ?
X = [1], Y = [2] ? ;
X = [1,2], Y = [2] ? ;
X = [2], Y = [1] ? ;
X = [2], Y = [1,2] ? ;
no
The code i am using for merge right now is
merge( [], RS, RS ).
merge( LS, [], LS ).
merge( [L|LS], [R|RS], [L|T] ) :- L =< R, merge( LS, [R|RS], T).
merge( [L|LS], [R|RS], [R|T] ) :- L > R, merge( [L|LS], RS, T).
Thanks in advance
Move the comparison (=</>) after the recursive call:
merge( [], RS, RS ).
merge( LS, [], LS ).
merge( [L|LS], [R|RS], [L|T] ) :- merge( LS, [R|RS], T), L =< R.
merge( [L|LS], [R|RS], [R|T] ) :- merge( [L|LS], RS, T), L > R.
=</> do not work with unbound variables, but if you move the comparison after the call to merge, the variables L and R will be bound to some value by the recursive call.
?- merge([1,2,3],[2,4,6],X).
X = [1, 2, 2, 3, 4, 6] ;
false.
?- merge([1,2,3],X,[1,2,3,4]).
X = [4] ;
false.
?- merge(Y,X,[1,2,3,4]).
Y = [],
X = [1, 2, 3, 4] ;
Y = [1, 2, 3, 4],
X = [] ;
Y = [1],
X = [2, 3, 4] ;
Y = [1, 2],
X = [3, 4] ;
...

Summing up special columns of two matrix in Matlab

How can I do below calculations (finding C) without using "for loop"?
[4, 2, 1, 7;
A = 0, 3, 4, 0;
8, 0, 10, 12;
11, 6, 2, 5];
[1, 0, 0, 4;
B = 0, 3, 2, 0;
5, 0, 8, 10;
7, 2, 1, 2];
C(i,j)= B(i,j-1) - B(i,j+1) + A(i,j+1); %if j is not equal to 4(number of columns) and it is not equal to 1
C(i,j)= B(i,4) - B(i,j+1) + A(i,j+1); %if j is equal to 1
C(i,j)= B(i,j-1) - B(i,1) + A(i,1); %if j is equal to 4(number of columns)
You can specify an array as index to work on multiple element at the same time:
A=[4,2,1,7;0,3,4,0;8,0,10,12;11,6,2,5];
B=[1,0,0,4;0,3,2,0;5,0,8,10;7,2,1,2];
C = zeros(size(A));
C(:,2:end-1) = B(:,1:end-2) - B(:,3:end) + A(:,3:end); %if j is not equal to 4(number of columns) and it is not equal to 1
j = 1;
C(:,j)= B(:,4) - B(:,j+1) + A(:,j+1); %if j is equal to 1
j = 4;
C(:,j)= B(:,j-1) - B(:,1) + A(:,1); %if j is equal to 4(number of columns)

Make matrix from shifted vector matlab vectorization

Geetings, here's the little problem.
I have a vector v (size(v) = T), a positive number P, P < T nad positive number N, N < T. And want to make matrix M with size PxN, so that:
M = [v(T), v(T-1), ....., v(T-N+2), v(T-N+1);
v(T-1), v(T-2), ....., v(T-N+1), v(T-N) ;
v(T-2), v(T-3), ....., v(T-N), v(T-N-1);
. . . . . ;
. . . . . ;
v(T-P+1), v(T-P), ....., v(T-P-N+3), v(T-P-N+2)]
It holds that T > P + N.
Let given an example.
v = [1, 2, 3, 4, 5]';
P = 3;
N = 3;
M = [5, 4, 3;
4, 3, 2;
3, 2, 1]
I know how to do this using for loop, but I also think that it is possible do by vectorizations - here the problem is - I am not so skilled in vectorizations.
Thx for hints and so on :))
Use the Hankel matrix:
v = [1 2 3 4 5];
T = length(v);
P = 3;
N = 3;
out = hankel(v(T:-1:T-P+1),v(T-P+1:-1:T-P-N+2));
You can make use of bsxfun
v = [1, 2, 3, 4, 5]';
P = 3;
N = 3;
idx = bsxfun(#minus, length(v):-1:length(v)-N+1, (0:P-1)');
result = v(idx);
this will result in
result =
5 4 3
4 3 2
3 2 1