Find maximum, minimum, average of a matrix in C++ [closed]

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How do I find the maximum, minimum and average values in a given matrix (matrix[i][j]) in C++. The type is unsigned long double.

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There's nothing clever to be done here (pseudocode only, since this smells like HW):

for each entry in the matrix:
    add the entry to a running sum
    compare the entry to a running min
        if it's smaller, it's the new running min
    compare the entry to a running max
        if it's larger, it's the new running max
average is the sum divided by the number of entries

You can micro-optimize that loop to make it more or less efficient, but there's nothing you can do algorithmically to be more clever. You need to look at all i*j entries no matter what.

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Maybe this:

Maximum:

int maximum = 0;
for(int x=0; x<width; ++x)
  for(int y=0; y<height; ++y)
    maximum = std::max(matrix[x][y], maximum);

Minimum:

int minimum = 0;
for(int x=0; x<width; ++x)
  for(int y=0; y<height; ++y)
    minimum = std::min(matrix[x][y], minimum);

Avarage:

int avarage = 0;
for(int x=0; x<width; ++x)
  for(int y=0; y<height; ++y)
    avarge += matrix[x][y];
avarge /= width*height;

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Assuming matrix is an actual C++ two-dimensional array could you use standard algorithms.

Untested code:

long double mean = std::accumulate(matrix[0], matrix[0] + i*j, 0.0) / (i*j);
long double matrix_min = std::min_element(matrix[0], matrix[0] + i*j);
long double matrix_max = std::max_element(matrix[0], matrix[0] + i*j);

Do note that this does extra passes over the matrix at the benefit of being clear what it's doing.

If it's another container type like a vector of vectors then you'd have to run the algorithms on each row and take the max of each row.

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Loop through all the values, recording current max, min, and cumulative sum. Then divide the cumulative sum by the number of elements to get the mean.