问题
I have an N*N matrix (N=2 to 10000) of numbers that may range from 0 to 1000. How can I find the largest (rectangular) submatrix that consists of the same number?
Example:
1 2 3 4 5
-- -- -- -- --
1 | 10 9 9 9 80
2 | 5 9 9 9 10
3 | 85 86 54 45 45
4 | 15 21 5 1 0
5 | 5 6 88 11 10
The output should be the area of the submatrix, followed by 1-based coordinates of its top left element. For the example, it would be (6, 2, 1) because there are six 9s situated at column 2, row 1.
回答1:
This is a work in progress
I thought about this problem and I think I may have a O(w*h) algorithm.
The idea goes like this:
- for any
(i,j)compute the highest number of cells with the same value in the columnjstarting from(i,j). Store this values asheights[i][j]. - create an empty vector of sub matrix (a lifo)
- for all row: i
- for all column: j
- pop all sub matrix whose
height > heights[i][j]. Because the submatrix with height >heights[i][j]cannot continue on this cell - push a submatrix defined by
(i,j,heights[i][j])wherejis the farest coordinate where we can fit a submatrix of height:heights[i][j] - update the current max sub matrix
- pop all sub matrix whose
- for all column: j
The tricky part is in the inner loop. I use something similar to the max subwindow algorithm to ensure it is O(1) on average for each cell.
I will try to formulate a proof but in the meantime here is the code.
#include <algorithm>
#include <iterator>
#include <iostream>
#include <ostream>
#include <vector>
typedef std::vector<int> row_t;
typedef std::vector<row_t> matrix_t;
std::size_t height(matrix_t const& M) { return M.size(); }
std::size_t width (matrix_t const& M) { return M.size() ? M[0].size() : 0u; }
std::ostream& operator<<(std::ostream& out, matrix_t const& M) {
for(unsigned i=0; i<height(M); ++i) {
std::copy(begin(M[i]), end(M[i]),
std::ostream_iterator<int>(out, ", "));
out << std::endl;
}
return out;
}
struct sub_matrix_t {
int i, j, h, w;
sub_matrix_t(): i(0),j(0),h(0),w(1) {}
sub_matrix_t(int i_,int j_,int h_,int w_):i(i_),j(j_),h(h_),w(w_) {}
bool operator<(sub_matrix_t const& rhs) const { return (w*h)<(rhs.w*rhs.h); }
};
// Pop all sub_matrix from the vector keeping only those with an height
// inferior to the passed height.
// Compute the max sub matrix while removing sub matrix with height > h
void pop_sub_m(std::vector<sub_matrix_t>& subs,
int i, int j, int h, sub_matrix_t& max_m) {
sub_matrix_t sub_m(i, j, h, 1);
while(subs.size() && subs.back().h >= h) {
sub_m = subs.back();
subs.pop_back();
sub_m.w = j-sub_m.j;
max_m = std::max(max_m, sub_m);
}
// Now sub_m.{i,j} is updated to the farest coordinates where there is a
// submatrix with heights >= h
// If we don't cut the current height (because we changed value) update
// the current max submatrix
if(h > 0) {
sub_m.h = h;
sub_m.w = j-sub_m.j+1;
max_m = std::max(max_m, sub_m);
subs.push_back(sub_m);
}
}
void push_sub_m(std::vector<sub_matrix_t>& subs,
int i, int j, int h, sub_matrix_t& max_m) {
if(subs.empty() || subs.back().h < h)
subs.emplace_back(i, j, h, 1);
}
void solve(matrix_t const& M, sub_matrix_t& max_m) {
// Initialize answer suitable for an empty matrix
max_m = sub_matrix_t();
if(height(M) == 0 || width(M) == 0) return;
// 1) Compute the heights of columns of the same values
matrix_t heights(height(M), row_t(width(M), 1));
for(unsigned i=height(M)-1; i>0; --i)
for(unsigned j=0; j<width(M); ++j)
if(M[i-1][j]==M[i][j])
heights[i-1][j] = heights[i][j]+1;
// 2) Run through all columns heights to compute local sub matrices
std::vector<sub_matrix_t> subs;
for(int i=height(M)-1; i>=0; --i) {
push_sub_m(subs, i, 0, heights[i][0], max_m);
for(unsigned j=1; j<width(M); ++j) {
bool same_val = (M[i][j]==M[i][j-1]);
int pop_height = (same_val) ? heights[i][j] : 0;
int pop_j = (same_val) ? j : j-1;
pop_sub_m (subs, i, pop_j, pop_height, max_m);
push_sub_m(subs, i, j, heights[i][j], max_m);
}
pop_sub_m(subs, i, width(M)-1, 0, max_m);
}
}
matrix_t M1{
{10, 9, 9, 9, 80},
{ 5, 9, 9, 9, 10},
{85, 86, 54, 45, 45},
{15, 21, 5, 1, 0},
{ 5, 6, 88, 11, 10},
};
matrix_t M2{
{10, 19, 9, 29, 80},
{ 5, 9, 9, 9, 10},
{ 9, 9, 54, 45, 45},
{ 9, 9, 5, 1, 0},
{ 5, 6, 88, 11, 10},
};
int main() {
sub_matrix_t answer;
std::cout << M1 << std::endl;
solve(M1, answer);
std::cout << '(' << (answer.w*answer.h)
<< ',' << (answer.j+1) << ',' << (answer.i+1) << ')'
<< std::endl;
answer = sub_matrix_t();
std::cout << M2 << std::endl;
solve(M2, answer);
std::cout << '(' << (answer.w*answer.h)
<< ',' << (answer.j+1) << ',' << (answer.i+1) << ')'
<< std::endl;
}
回答2:
This is an order Rows*Columns Solution
It works by
- starting at the bottom of the array, and determining how many items below each number match it in a column. This is done in O(MN) time (very trivially)
- Then it goes top to bottom & left to right and sees if any given number matches the number to the left. If so, it keeps track of how the heights relate to each other to track the possible rectangle shapes
Here is a working python implementation. Apologies since I'm not sure how to get the syntax highlighting working
# this program finds the largest area in an array where all the elements have the same value
# It solves in O(rows * columns) time using O(rows*columns) space using dynamic programming
def max_area_subarray(array):
rows = len(array)
if (rows == 0):
return [[]]
columns = len(array[0])
# initialize a blank new array
# this will hold max elements of the same value in a column
new_array = []
for i in range(0,rows-1):
new_array.append([0] * columns)
# start with the bottom row, these all of 1 element of the same type
# below them, including themselves
new_array.append([1] * columns)
# go from the second to bottom row up, finding how many contiguous
# elements of the same type there are
for i in range(rows-2,-1,-1):
for j in range(columns-1,-1,-1):
if ( array[i][j] == array[i+1][j]):
new_array[i][j] = new_array[i+1][j]+1
else:
new_array[i][j] = 1
# go left to right and match up the max areas
max_area = 0
top = 0
bottom = 0
left = 0
right = 0
for i in range(0,rows):
running_height =[[0,0,0]]
for j in range(0,columns):
matched = False
if (j > 0): # if this isn't the leftmost column
if (array[i][j] == array[i][j-1]):
# this matches the array to the left
# keep track of if this is a longer column, shorter column, or same as
# the one on the left
matched = True
while( new_array[i][j] < running_height[-1][0]):
# this is less than the one on the left, pop that running
# height from the list, and add it's columns to the smaller
# running height below it
if (running_height[-1][1] > max_area):
max_area = running_height[-1][1]
top = i
right = j-1
bottom = i + running_height[-1][0]-1
left = j - running_height[-1][2]
previous_column = running_height.pop()
num_columns = previous_column[2]
if (len(running_height) > 0):
running_height[-1][1] += running_height[-1][0] * (num_columns)
running_height[-1][2] += num_columns
else:
# for instance, if we have heights 2,2,1
# this will trigger on the 1 after we pop the 2 out, and save the current
# height of 1, the running area of 3, and running columsn of 3
running_height.append([new_array[i][j],new_array[i][j]*(num_columns),num_columns])
if (new_array[i][j] > running_height[-1][0]):
# longer then the one on the left
# append this height and area
running_height.append([new_array[i][j],new_array[i][j],1])
elif (new_array[i][j] == running_height[-1][0]):
# same as the one on the left, add this area to the one on the left
running_height[-1][1] += new_array[i][j]
running_height[-1][2] += 1
if (matched == False or j == columns -1):
while(running_height):
# unwind the maximums & see if this is the new max area
if (running_height[-1][1] > max_area):
max_area = running_height[-1][1]
top = i
right = j
bottom = i + running_height[-1][0]-1
left = j - running_height[-1][2]+1
# this wasn't a match, so move everything one bay to the left
if (matched== False):
right = right-1
left = left-1
previous_column = running_height.pop()
num_columns = previous_column[2]
if (len(running_height) > 0):
running_height[-1][1] += running_height[-1][0] * num_columns
running_height[-1][2] += num_columns
if (matched == False):
# this is either the left column, or we don't match to the column to the left, so reset
running_height = [[new_array[i][j],new_array[i][j],1]]
if (running_height[-1][1] > max_area):
max_area = running_height[-1][1]
top = i
right = j
bottom = i + running_height[-1][0]-1
left = j - running_height[-1][2]+1
max_array = []
for i in range(top,bottom+1):
max_array.append(array[i][left:right+1])
return max_array
numbers = [[6,4,1,9],[5,2,2,7],[2,2,2,1],[2,3,1,5]]
for row in numbers:
print row
print
print
max_array = max_area_subarray(numbers)
max_area = len(max_array) * len(max_array[0])
print 'max area is ',max_area
print
for row in max_array:
print row
来源：https://stackoverflow.com/questions/2301733/find-largest-submatrix-algorithm