从线性的角度来看，考虑以下矩阵:

    1 2 3        0 0 1
A = 4 5 6    B = 0 1 0
    7 8 9        1 0 0

现在求A

     1 4 7
A' = 2 5 8
     3 6 9

考虑A'对B的作用，或B对A'的作用。 分别为:

      7 4 1          3 6 9
A'B = 8 5 2    BA' = 2 5 8
      9 6 3          1 4 7

这对任何nxn矩阵都是可展开的。 在代码中快速应用这个概念:

void swapInSpace(int** mat, int r1, int c1, int r2, int c2)
{
    mat[r1][c1] ^= mat[r2][c2];
    mat[r2][c2] ^= mat[r1][c1];
    mat[r1][c1] ^= mat[r2][c2];
}

void transpose(int** mat, int size)
{
    for (int i = 0; i < size; i++)
    {
        for (int j = (i + 1); j < size; j++)
        {
            swapInSpace(mat, i, j, j, i);
        }
    }
}

void rotate(int** mat, int size)
{
    //Get transpose
    transpose(mat, size);

    //Swap columns
    for (int i = 0; i < size / 2; i++)
    {
        for (int j = 0; j < size; j++)
        {
            swapInSpace(mat, i, j, size - (i + 1), j);
        }
    }
}

在python中:

import numpy as np

a = np.array(
    [
        [1, 2, 3, 4],
        [5, 6, 7, 8],
        [9, 0, 1, 2],
        [3, 4, 5, 6]
    ]
)

print(a)
print(b[::-1, :].T)

为新手程序员，在纯c++。(宝蓝的东西)

#include<iostream.h>
#include<conio.h>

int main()
{
    clrscr();

    int arr[10][10];        // 2d array that holds input elements 
    int result[10][10];     //holds result

    int m,n;                //rows and columns of arr[][]
    int x,y;                //rows and columns of result[][]

    int i,j;                //loop variables
    int t;                  //temporary , holds data while conversion

    cout<<"Enter no. of rows and columns of array: ";
    cin>>m>>n;
    cout<<"\nEnter elements of array: \n\n";
    for(i = 0; i < m; i++)
    {
        for(j = 0; j<n ; j++)
        {
          cin>>arr[i][j];         // input array elements from user
        }
    }


   //rotating matrix by +90 degrees

    x = n ;                      //for non-square matrix
    y = m ;     

    for(i = 0; i < x; i++)
    {  t = m-1;                     // to create required array bounds
       for(j = 0; j < y; j++)
       {
          result[i][j] = arr[t][i];
          t--;
       }
   }

   //print result

   cout<<"\nRotated matrix is: \n\n";
   for(i = 0; i < x; i++)
   {
       for(j = 0; j < y; j++)
       {
             cout<<result[i][j]<<" ";
       }
       cout<<"\n";
   }

   getch();
   return 0;
}

在Eigen (c++)中:

Eigen::Matrix2d mat;
mat <<  1, 2,
        3, 4;
std::cout << mat << "\n\n";

Eigen::Matrix2d r_plus_90 = mat.transpose().rowwise().reverse();
std::cout << r_plus_90 << "\n\n";

Eigen::Matrix2d r_minus_90 = mat.transpose().colwise().reverse();
std::cout << r_minus_90 << "\n\n";

Eigen::Matrix2d r_180 = mat.colwise().reverse().rowwise().reverse(); // +180 same as -180
std::cout << r_180 << "\n\n";

输出:

1 2
3 4

3 1
4 2

2 4
1 3

4 3
2 1

虽然旋转数据可能是必要的(也许是为了更新物理存储的表示)，但在数组访问上添加一层间接层(也许是一个接口)会变得更简单，可能更性能:

interface IReadableMatrix
{
    int GetValue(int x, int y);
}

如果你的矩阵已经实现了这个接口，那么它可以通过这样一个装饰器类来旋转:

class RotatedMatrix : IReadableMatrix
{
    private readonly IReadableMatrix _baseMatrix;

    public RotatedMatrix(IReadableMatrix baseMatrix)
    {
        _baseMatrix = baseMatrix;
    }

    int GetValue(int x, int y)
    {
        // transpose x and y dimensions
        return _baseMatrix(y, x);
    }
}

旋转+90/-90/180度，水平/垂直翻转和缩放都可以以这种方式实现。

Performance would need to be measured in your specific scenario. However the O(n^2) operation has now been replaced with an O(1) call. It's a virtual method call which is slower than direct array access, so it depends upon how frequently the rotated array is used after rotation. If it's used once, then this approach would definitely win. If it's rotated then used in a long-running system for days, then in-place rotation might perform better. It also depends whether you can accept the up-front cost.

与所有性能问题一样，测量，测量，测量!

Nick的答案也适用于NxM阵列，只需要做一点修改(与NxN相反)。

string[,] orig = new string[n, m];
string[,] rot = new string[m, n];

...

for ( int i=0; i < n; i++ )
  for ( int j=0; j < m; j++ )
    rot[j, n - i - 1] = orig[i, j];

考虑这个问题的一种方法是将轴(0,0)的中心从左上角移动到右上角。你只是简单地从一个转置到另一个。