The max product of consecutive elements in an array

You can implement a variant of the Kadane algorithm (http://en.wikipedia.org/wiki/Maximum_subarray_problem) who runs with constant extra memory and linear in the size of the problem (no extra array,...)

If only strict positive numbers are given:

def max_subarray_mul(A):
    max_ending_here = max_so_far = 1
    for x in A:
        if x > 0
            max_ending_here = max(1,max_ending_here*x)
            max_so_far = max(max_so_far, max_ending_here)
    return max_so_far

I'm still working on the part with negative numbers

Or a more expensive (in time) method is the following, but this will work with negative numbers:

def max_subarray_mul(A):
    max_so_far = 1
    n = length(A)
    for i in 1...n:
        x = A[i]
        tmp = x
        max_so_far = max(max_so_far,tmp)
        for j in i+1...n:
          tmp = tmp*A[j]
          max_so_far = max(max_so_far,tmp)
    return max_so_far

Which runs in constant memory and O(n²) time

The algorithm is indeed O(n). When iterating the array, use a variable to store the max value found so far, a variable to store the max value of subarray that ends at a[i], and another variable to store minimum value that ends at a[i] to treat negative values.

float find_maximum(float arr[], int n) {
    if (n <= 0) return NAN;

    float max_at = arr[0];  // Maximum value that ends at arr[i]
    float min_at = arr[0];  // Minimum value that ends at arr[i]
    float max_value = max_at;

    for (int i = 1; i < n; i++) {
        float prev_max_at = max_at, prev_min_at = min_at;
        max_at = max(arr[i], arr[i] * prev_min_at, arr[i] * prev_max_at);
        min_at = min(arr[i], arr[i] * prev_min_at, arr[i] * prev_max_at);
        max_value = max(max_value, max_at);
    }
    return max_value;
}