import sys

"""
Dynamic Programming
Implementation of Matrix Chain Multiplication
Time Complexity: O(n^3)
Space Complexity: O(n^2)
"""


def MatrixChainOrder(array):
    N = len(array)
    Matrix = [[0 for x in range(N)] for x in range(N)]
    Sol = [[0 for x in range(N)] for x in range(N)]

    for ChainLength in range(2, N):
        for a in range(1, N - ChainLength + 1):
            b = a + ChainLength - 1

            Matrix[a][b] = sys.maxsize
            for c in range(a, b):
                cost = (
                    Matrix[a][c] + Matrix[c + 1][b] + array[a - 1] * array[c] * array[b]
                )
                if cost < Matrix[a][b]:
                    Matrix[a][b] = cost
                    Sol[a][b] = c
    return Matrix, Sol


# Print order of matrix with Ai as Matrix
def PrintOptimalSolution(OptimalSolution, i, j):
    if i == j:
        print("A" + str(i), end=" ")
    else:
        print("(", end=" ")
        PrintOptimalSolution(OptimalSolution, i, OptimalSolution[i][j])
        PrintOptimalSolution(OptimalSolution, OptimalSolution[i][j] + 1, j)
        print(")", end=" ")


def main():
    array = [30, 35, 15, 5, 10, 20, 25]
    n = len(array)
    # Size of matrix created from above array will be
    # 30*35 35*15 15*5 5*10 10*20 20*25
    Matrix, OptimalSolution = MatrixChainOrder(array)

    print("No. of Operation required: " + str(Matrix[1][n - 1]))
    PrintOptimalSolution(OptimalSolution, 1, n - 1)


if __name__ == "__main__":
    main()