已经有很多答案了，我发现两个声称O(1)时间复杂度。真正的O(1)算法是保持数组存储不变，并改变索引其元素的方式。这里的目标是不消耗额外的内存，也不需要额外的时间来迭代数据。

旋转90度，-90度和180度是简单的转换，只要你知道你的2D数组中有多少行和列就可以执行;要将任何向量旋转90度，交换轴并与Y轴相反。对于-90度，交换轴和X轴。对于180度，两个坐标轴都是负的，不交换。

进一步的转换是可能的，例如通过独立地否定轴来水平和/或垂直地镜像。

这可以通过访问器方法来实现。下面的例子是JavaScript函数，但是这些概念同样适用于所有语言。

//按列/行顺序获取数组元素 var getArray2d =函数(a, x, y) { 返回一个[y] [x]; }; / /演示 Var arr = [ [5,4,6]， [1,7,9]， [- 2,11,0]， [8,21， -3]， [3， -1, 2] ]; Var newar = []; arr[0]. foreach (() => newarr。push(新数组(arr.length))); For (var I = 0;I < newar .length;我+ +){ For (var j = 0;J < newarr[0].length;j + +) { newarr[i][j] = getArray2d(arr, i, j); ｝ ｝ console.log (newarr);

// Get an array element rotated 90 degrees clockwise function getArray2dCW(a, x, y) { var t = x; x = y; y = a.length - t - 1; return a[y][x]; } //demo var arr = [ [5, 4, 6], [1, 7, 9], [-2, 11, 0], [8, 21, -3], [3, -1, 2] ]; var newarr = []; arr[0].forEach(() => newarr.push(new Array(arr.length))); for (var i = 0; i < newarr[0].length; i++) { for (var j = 0; j < newarr.length; j++) { newarr[j][i] = getArray2dCW(arr, i, j); } } console.log(newarr);

// Get an array element rotated 90 degrees counter-clockwise function getArray2dCCW(a, x, y) { var t = x; x = a[0].length - y - 1; y = t; return a[y][x]; } //demo var arr = [ [5, 4, 6], [1, 7, 9], [-2, 11, 0], [8, 21, -3], [3, -1, 2] ]; var newarr = []; arr[0].forEach(() => newarr.push(new Array(arr.length))); for (var i = 0; i < newarr[0].length; i++) { for (var j = 0; j < newarr.length; j++) { newarr[j][i] = getArray2dCCW(arr, i, j); } } console.log(newarr);

// Get an array element rotated 180 degrees function getArray2d180(a, x, y) { x = a[0].length - x - 1; y = a.length - y - 1; return a[y][x]; } //demo var arr = [ [5, 4, 6], [1, 7, 9], [-2, 11, 0], [8, 21, -3], [3, -1, 2] ]; var newarr = []; arr.forEach(() => newarr.push(new Array(arr[0].length))); for (var i = 0; i < newarr[0].length; i++) { for (var j = 0; j < newarr.length; j++) { newarr[j][i] = getArray2d180(arr, i, j); } } console.log(newarr);

这段代码假设有一个嵌套数组的数组，其中每个内部数组都是一行。

该方法允许您读取(或写入)元素(甚至是随机顺序)，就像数组已经旋转或转换一样。现在只要选择正确的函数来调用，可能是通过引用，然后就可以了!

这个概念可以扩展为通过访问器方法附加地(非破坏性地)应用转换。包括任意角度旋转和缩放。

在原地顺时针90度旋转使用矢量矢量..

 #include<iostream>
 #include<vector>
 #include<algorithm>
 using namespace std;
 //Rotate a Matrix by 90 degrees
void rotateMatrix(vector<vector<int> > &matrix){
   int n=matrix.size();
   for(int i=0;i<n;i++){
    for(int j=i+1;j<n;j++){
        swap(matrix[i][j],matrix[j][i]);
    }
 }
     for(int i=0;i<n;i++){
        reverse(matrix[i].begin(),matrix[i].end());
       }
   }

    int main(){

   int n;
   cout<<"enter the size of the matrix:"<<endl;
     while (cin >> n) {
    vector< vector<int> > m;
      cout<<"enter the elements"<<endl;
    for (int i = 0; i < n; i++) {
        m.push_back(vector<int>(n));
        for (int j = 0; j < n; j++)
            scanf("%d", &m[i][j]);
    }
      cout<<"the rotated matrix is:"<<endl;
      rotateMatrix(m);
    for (int i = 0; i < n; i++) {
        for (int j = 0; j < n; j++)
            cout << m[i][j] << ' ';
        cout << endl;
    }
   }
   return 0;
 }

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);
        }
    }
}

矩阵转置和旋转(+/-90，+/-180)的C代码

支持方阵和非方阵，具有原位和复制功能 支持2D数组和带有逻辑行/cols的1D指针 单元测试;有关使用示例，请参阅测试 测试gcc -std=c90 -Wall -pedantic, MSVC17

`

#include <stdlib.h>
#include <memory.h>
#include <assert.h>

/* 
    Matrix transpose & rotate (+/-90, +/-180)
        Supports both 2D arrays and 1D pointers with logical rows/cols
        Supports square and non-square matrices, has in-place and copy features
        See tests for examples of usage
    tested gcc -std=c90 -Wall -pedantic, MSVC17
*/

typedef int matrix_data_t;  /* matrix data type */

void transpose(const matrix_data_t* src, matrix_data_t* dst, int rows, int cols);
void transpose_inplace(matrix_data_t* data, int n );
void rotate(int direction, const matrix_data_t* src, matrix_data_t* dst, int rows, int cols);
void rotate_inplace(int direction, matrix_data_t* data, int n);
void reverse_rows(matrix_data_t* data, int rows, int cols);
void reverse_cols(matrix_data_t* data, int rows, int cols);

/* test/compare fn */
int test_cmp(const matrix_data_t* lhs, const matrix_data_t* rhs, int rows, int cols );

/* TESTS/USAGE */
void transpose_test() {

    matrix_data_t sq3x3[9] = { 0,1,2,3,4,5,6,7,8 };/* 3x3 square, odd length side */
    matrix_data_t sq3x3_cpy[9];
    matrix_data_t sq3x3_2D[3][3] = { { 0,1,2 },{ 3,4,5 },{ 6,7,8 } };/* 2D 3x3 square */
    matrix_data_t sq3x3_2D_copy[3][3];

    /* expected test values */
    const matrix_data_t sq3x3_orig[9] = { 0,1,2,3,4,5,6,7,8 };
    const matrix_data_t sq3x3_transposed[9] = { 0,3,6,1,4,7,2,5,8};

    matrix_data_t sq4x4[16]= { 0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15 };/* 4x4 square, even length*/
    const matrix_data_t sq4x4_orig[16] = { 0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15 };
    const matrix_data_t sq4x4_transposed[16] = { 0,4,8,12,1,5,9,13,2,6,10,14,3,7,11,15 };

    /* 2x3 rectangle */
    const matrix_data_t r2x3_orig[6] = { 0,1,2,3,4,5 };
    const matrix_data_t r2x3_transposed[6] = { 0,3,1,4,2,5 };
    matrix_data_t r2x3_copy[6];

    matrix_data_t r2x3_2D[2][3] = { {0,1,2},{3,4,5} };  /* 2x3 2D rectangle */
    matrix_data_t r2x3_2D_t[3][2];

    /* matrix_data_t r3x2[6] = { 0,1,2,3,4,5 }; */
    matrix_data_t r3x2_copy[6];
    /* 3x2 rectangle */
    const matrix_data_t r3x2_orig[6] = { 0,1,2,3,4,5 };
    const matrix_data_t r3x2_transposed[6] = { 0,2,4,1,3,5 };

    matrix_data_t r6x1[6] = { 0,1,2,3,4,5 };    /* 6x1 */
    matrix_data_t r6x1_copy[6];

    matrix_data_t r1x1[1] = { 0 };  /*1x1*/
    matrix_data_t r1x1_copy[1];

    /* 3x3 tests, 2D array tests */
    transpose_inplace(sq3x3, 3);    /* transpose in place */
    assert(!test_cmp(sq3x3, sq3x3_transposed, 3, 3));
    transpose_inplace(sq3x3, 3);    /* transpose again */
    assert(!test_cmp(sq3x3, sq3x3_orig, 3, 3));

    transpose(sq3x3, sq3x3_cpy, 3, 3);  /* transpose copy 3x3*/
    assert(!test_cmp(sq3x3_cpy, sq3x3_transposed, 3, 3));

    transpose((matrix_data_t*)sq3x3_2D, (matrix_data_t*)sq3x3_2D_copy, 3, 3);   /* 2D array transpose/copy */
    assert(!test_cmp((matrix_data_t*)sq3x3_2D_copy, sq3x3_transposed, 3, 3));
    transpose_inplace((matrix_data_t*)sq3x3_2D_copy, 3);    /* 2D array transpose in place */
    assert(!test_cmp((matrix_data_t*)sq3x3_2D_copy, sq3x3_orig, 3, 3));

    /* 4x4 tests */
    transpose_inplace(sq4x4, 4);    /* transpose in place */
    assert(!test_cmp(sq4x4, sq4x4_transposed, 4,4));
    transpose_inplace(sq4x4, 4);    /* transpose again */
    assert(!test_cmp(sq4x4, sq4x4_orig, 3, 3));

    /* 2x3,3x2 tests */
    transpose(r2x3_orig, r2x3_copy, 2, 3);
    assert(!test_cmp(r2x3_copy, r2x3_transposed, 3, 2));

    transpose(r3x2_orig, r3x2_copy, 3, 2);
    assert(!test_cmp(r3x2_copy, r3x2_transposed, 2,3));

    /* 2D array */
    transpose((matrix_data_t*)r2x3_2D, (matrix_data_t*)r2x3_2D_t, 2, 3);
    assert(!test_cmp((matrix_data_t*)r2x3_2D_t, r2x3_transposed, 3,2));

    /* Nx1 test, 1x1 test */
    transpose(r6x1, r6x1_copy, 6, 1);
    assert(!test_cmp(r6x1_copy, r6x1, 1, 6));

    transpose(r1x1, r1x1_copy, 1, 1);
    assert(!test_cmp(r1x1_copy, r1x1, 1, 1));

}

void rotate_test() {

    /* 3x3 square */
    const matrix_data_t sq3x3[9] = { 0,1,2,3,4,5,6,7,8 };
    const matrix_data_t sq3x3_r90[9] = { 6,3,0,7,4,1,8,5,2 };
    const matrix_data_t sq3x3_180[9] = { 8,7,6,5,4,3,2,1,0 };
    const matrix_data_t sq3x3_l90[9] = { 2,5,8,1,4,7,0,3,6 };
    matrix_data_t sq3x3_copy[9];

    /* 3x3 square, 2D */
    matrix_data_t sq3x3_2D[3][3] = { { 0,1,2 },{ 3,4,5 },{ 6,7,8 } };

    /* 4x4, 2D */
    matrix_data_t sq4x4[4][4] = { { 0,1,2,3 },{ 4,5,6,7 },{ 8,9,10,11 },{ 12,13,14,15 } };
    matrix_data_t sq4x4_copy[4][4];
    const matrix_data_t sq4x4_r90[16] = { 12,8,4,0,13,9,5,1,14,10,6,2,15,11,7,3 };
    const matrix_data_t sq4x4_l90[16] = { 3,7,11,15,2,6,10,14,1,5,9,13,0,4,8,12 };
    const matrix_data_t sq4x4_180[16] = { 15,14,13,12,11,10,9,8,7,6,5,4,3,2,1,0 };

    matrix_data_t r6[6] = { 0,1,2,3,4,5 };  /* rectangle with area of 6 (1x6,2x3,3x2, or 6x1) */
    matrix_data_t r6_copy[6];
    const matrix_data_t r1x6_r90[6] = { 0,1,2,3,4,5 };
    const matrix_data_t r1x6_l90[6] = { 5,4,3,2,1,0 };
    const matrix_data_t r1x6_180[6] = { 5,4,3,2,1,0 };

    const matrix_data_t r2x3_r90[6] = { 3,0,4,1,5,2 };
    const matrix_data_t r2x3_l90[6] = { 2,5,1,4,0,3 };
    const matrix_data_t r2x3_180[6] = { 5,4,3,2,1,0 };

    const matrix_data_t r3x2_r90[6] = { 4,2,0,5,3,1 };
    const matrix_data_t r3x2_l90[6] = { 1,3,5,0,2,4 };
    const matrix_data_t r3x2_180[6] = { 5,4,3,2,1,0 };

    const matrix_data_t r6x1_r90[6] = { 5,4,3,2,1,0 };
    const matrix_data_t r6x1_l90[6] = { 0,1,2,3,4,5 };
    const matrix_data_t r6x1_180[6] = { 5,4,3,2,1,0 };

    /* sq3x3 tests */
    rotate(90, sq3x3, sq3x3_copy, 3, 3);    /* +90 */
    assert(!test_cmp(sq3x3_copy, sq3x3_r90, 3, 3));
    rotate(-90, sq3x3, sq3x3_copy, 3, 3);   /* -90 */
    assert(!test_cmp(sq3x3_copy, sq3x3_l90, 3, 3));
    rotate(180, sq3x3, sq3x3_copy, 3, 3);   /* 180 */
    assert(!test_cmp(sq3x3_copy, sq3x3_180, 3, 3));
    /* sq3x3 in-place rotations */
    memcpy( sq3x3_copy, sq3x3, 3 * 3 * sizeof(matrix_data_t));
    rotate_inplace(90, sq3x3_copy, 3);
    assert(!test_cmp(sq3x3_copy, sq3x3_r90, 3, 3));
    rotate_inplace(-90, sq3x3_copy, 3);
    assert(!test_cmp(sq3x3_copy, sq3x3, 3, 3)); /* back to 0 orientation */
    rotate_inplace(180, sq3x3_copy, 3);
    assert(!test_cmp(sq3x3_copy, sq3x3_180, 3, 3));
    rotate_inplace(-180, sq3x3_copy, 3);
    assert(!test_cmp(sq3x3_copy, sq3x3, 3, 3));
    rotate_inplace(180, (matrix_data_t*)sq3x3_2D, 3);/* 2D test */
    assert(!test_cmp((matrix_data_t*)sq3x3_2D, sq3x3_180, 3, 3));

    /* sq4x4 */
    rotate(90, (matrix_data_t*)sq4x4, (matrix_data_t*)sq4x4_copy, 4, 4);
    assert(!test_cmp((matrix_data_t*)sq4x4_copy, sq4x4_r90, 4, 4));
    rotate(-90, (matrix_data_t*)sq4x4, (matrix_data_t*)sq4x4_copy, 4, 4);
    assert(!test_cmp((matrix_data_t*)sq4x4_copy, sq4x4_l90, 4, 4));
    rotate(180, (matrix_data_t*)sq4x4, (matrix_data_t*)sq4x4_copy, 4, 4);
    assert(!test_cmp((matrix_data_t*)sq4x4_copy, sq4x4_180, 4, 4));

    /* r6 as 1x6 */
    rotate(90, r6, r6_copy, 1, 6);
    assert(!test_cmp(r6_copy, r1x6_r90, 1, 6));
    rotate(-90, r6, r6_copy, 1, 6);
    assert(!test_cmp(r6_copy, r1x6_l90, 1, 6));
    rotate(180, r6, r6_copy, 1, 6);
    assert(!test_cmp(r6_copy, r1x6_180, 1, 6));

    /* r6 as 2x3 */
    rotate(90, r6, r6_copy, 2, 3);
    assert(!test_cmp(r6_copy, r2x3_r90, 2, 3));
    rotate(-90, r6, r6_copy, 2, 3);
    assert(!test_cmp(r6_copy, r2x3_l90, 2, 3));
    rotate(180, r6, r6_copy, 2, 3);
    assert(!test_cmp(r6_copy, r2x3_180, 2, 3));

    /* r6 as 3x2 */
    rotate(90, r6, r6_copy, 3, 2);
    assert(!test_cmp(r6_copy, r3x2_r90, 3, 2));
    rotate(-90, r6, r6_copy, 3, 2);
    assert(!test_cmp(r6_copy, r3x2_l90, 3, 2));
    rotate(180, r6, r6_copy, 3, 2);
    assert(!test_cmp(r6_copy, r3x2_180, 3, 2));

    /* r6 as 6x1 */
    rotate(90, r6, r6_copy, 6, 1);
    assert(!test_cmp(r6_copy, r6x1_r90, 6, 1));
    rotate(-90, r6, r6_copy, 6, 1);
    assert(!test_cmp(r6_copy, r6x1_l90, 6, 1));
    rotate(180, r6, r6_copy, 6, 1);
    assert(!test_cmp(r6_copy, r6x1_180, 6, 1));
}

/* test comparison fn, return 0 on match else non zero */
int test_cmp(const matrix_data_t* lhs, const matrix_data_t* rhs, int rows, int cols) {

    int r, c;

    for (r = 0; r < rows; ++r) {
        for (c = 0; c < cols; ++c) {
            if ((lhs + r * cols)[c] != (rhs + r * cols)[c])
                return -1;
        }
    }
    return 0;
}

/*
Reverse values in place of each row in 2D matrix data[rows][cols] or in 1D pointer with logical rows/cols
[A B C] ->  [C B A]
[D E F]     [F E D]
*/
void reverse_rows(matrix_data_t* data, int rows, int cols) {

    int r, c;
    matrix_data_t temp;
    matrix_data_t* pRow = NULL;

    for (r = 0; r < rows; ++r) {
        pRow = (data + r * cols);
        for (c = 0; c < (int)(cols / 2); ++c) { /* explicit truncate */
            temp = pRow[c];
            pRow[c] = pRow[cols - 1 - c];
            pRow[cols - 1 - c] = temp;
        }
    }
}

/*
Reverse values in place of each column in 2D matrix data[rows][cols] or in 1D pointer with logical rows/cols
[A B C] ->  [D E F]
[D E F]     [A B C]
*/
void reverse_cols(matrix_data_t* data, int rows, int cols) {

    int r, c;
    matrix_data_t temp;
    matrix_data_t* pRowA = NULL;
    matrix_data_t* pRowB = NULL;

    for (c = 0; c < cols; ++c) {
        for (r = 0; r < (int)(rows / 2); ++r) { /* explicit truncate */
            pRowA = data + r * cols;
            pRowB = data + cols * (rows - 1 - r);
            temp = pRowA[c];
            pRowA[c] = pRowB[c];
            pRowB[c] = temp;
        }
    }
}

/* Transpose NxM matrix to MxN matrix in O(n) time */
void transpose(const matrix_data_t* src, matrix_data_t* dst, int N, int M) {

    int i;
    for (i = 0; i<N*M; ++i) dst[(i%M)*N + (i / M)] = src[i];    /* one-liner version */

    /*
    expanded version of one-liner:  calculate XY based on array index, then convert that to YX array index
    int i,j,x,y;
    for (i = 0; i < N*M; ++i) {
    x = i % M;
    y = (int)(i / M);
    j = x * N + y;
    dst[j] = src[i];
    }
    */

    /*
    nested for loop version
    using ptr arithmetic to get proper row/column
    this is really just dst[col][row]=src[row][col]

    int r, c;

    for (r = 0; r < rows; ++r) {
        for (c = 0; c < cols; ++c) {
            (dst + c * rows)[r] = (src + r * cols)[c];
        }
    }
    */
}

/*
Transpose NxN matrix in place
*/
void transpose_inplace(matrix_data_t* data, int N ) {

    int r, c;
    matrix_data_t temp;

    for (r = 0; r < N; ++r) {
        for (c = r; c < N; ++c) { /*start at column=row*/
                                    /* using ptr arithmetic to get proper row/column */
                                    /* this is really just
                                    temp=dst[col][row];
                                    dst[col][row]=src[row][col];
                                    src[row][col]=temp;
                                    */
            temp = (data + c * N)[r];
            (data + c * N)[r] = (data + r * N)[c];
            (data + r * N)[c] = temp;
        }
    }
}

/*
Rotate 1D or 2D src matrix to dst matrix in a direction (90,180,-90)
Precondition:  src and dst are 2d matrices with dimensions src[rows][cols] and dst[cols][rows] or 1D pointers with logical rows/cols
*/
void rotate(int direction, const matrix_data_t* src, matrix_data_t* dst, int rows, int cols) {

    switch (direction) {
    case -90:
        transpose(src, dst, rows, cols);
        reverse_cols(dst, cols, rows);
        break;
    case 90:
        transpose(src, dst, rows, cols);
        reverse_rows(dst, cols, rows);
        break;
    case 180:
    case -180:
        /* bit copy to dst, use in-place reversals */
        memcpy(dst, src, rows*cols*sizeof(matrix_data_t));
        reverse_cols(dst, cols, rows);
        reverse_rows(dst, cols, rows);
        break;
    }
}

/*
Rotate array in a direction.
Array must be NxN 2D or 1D array with logical rows/cols
Direction can be (90,180,-90,-180)
*/
void rotate_inplace( int direction, matrix_data_t* data, int n) {

    switch (direction) {
    case -90:
        transpose_inplace(data, n);
        reverse_cols(data, n, n);
        break;
    case 90:
        transpose_inplace(data, n);
        reverse_rows(data, n, n);
        break;
    case 180:
    case -180:
        reverse_cols(data, n, n);
        reverse_rows(data, n, n);
        break;
    }
}

`

下面是一个原地旋转的数组，而不是使用一个全新的数组来保存结果。我已经停止了数组的初始化和输出。这只适用于正方形数组，但它们可以是任何大小。内存开销等于数组中一个元素的大小，因此您可以对任意大的数组进行旋转。

int a[4][4];
int n = 4;
int tmp;
for (int i = 0; i < n / 2; i++)
{
    for (int j = i; j < n - i - 1; j++)
    {
        tmp             = a[i][j];
        a[i][j]         = a[j][n-i-1];
        a[j][n-i-1]     = a[n-i-1][n-j-1];
        a[n-i-1][n-j-1] = a[n-j-1][i];
        a[n-j-1][i]     = tmp;
    }
}

下面是Java版本:

public static void rightRotate(int[][] matrix, int n) {
    for (int layer = 0; layer < n / 2; layer++) {
        int first = layer;
        int last = n - 1 - first;
        for (int i = first; i < last; i++) {
           int offset = i - first;
           int temp = matrix[first][i];
           matrix[first][i] = matrix[last-offset][first];
           matrix[last-offset][first] = matrix[last][last-offset];
           matrix[last][last-offset] = matrix[i][last];
           matrix[i][last] = temp;
        }
    }
}

该方法首先旋转最外层，然后按顺序移动到内层。