Go: package main

import (
	"bufio"
	"fmt"
	"math"
	"os"
	"strconv"
)

type Matrix struct {
	r int // num of row
	c int // num of column
}

type Range struct {
	start int // index, inclusive
	end   int // index, inclusive
}

func (r Range) length() int {
	return r.end - r.start + 1
}

func calcMultiplication(x, y Matrix) (Matrix, int) {
	if x.c != y.r {
		panic("cannnot calculate")
	}
	return Matrix{x.r, y.c}, x.r * y.c * x.c
}

func calcMinMatrixChainMultiplication(matrices []Matrix, r Range, memo map[Range]int) (Matrix, int) {
	if v, ok := memo[r]; ok {
		return Matrix{matrices[r.start].r, matrices[r.end].c}, v
	}
	if r.length() == 0 {
		panic("must not be empty")
	} else if r.length() == 1 {
		return matrices[r.start], 0
	}

	var minCount int = math.MaxInt32

	for i := 0; i < r.length()-1; i++ {
		formerRange := Range{r.start, r.start + i}
		latterRange := Range{r.start + i + 1, r.end}
		matF, countF := calcMinMatrixChainMultiplication(matrices, formerRange, memo)
		matL, countL := calcMinMatrixChainMultiplication(matrices, latterRange, memo)
		_, countFL := calcMultiplication(matF, matL)
		count := countF + countL + countFL
		if count < minCount {
			minCount = count
		}
	}
	memo[r] = minCount
	return Matrix{matrices[r.start].r, matrices[r.end].c}, minCount
}

func main() {
	sc := bufio.NewScanner(os.Stdin)
	sc.Split(bufio.ScanWords)
	n, _ := nextInt(sc)
	matrices := make([]Matrix, n)
	for i := 0; i < n; i++ {
		r, _ := nextInt(sc)
		c, _ := nextInt(sc)
		matrices[i] = Matrix{r, c}
	}
	memo := make(map[Range]int)
	_, resultCount := calcMinMatrixChainMultiplication(matrices, Range{0, len(matrices) - 1}, memo)
	fmt.Println(resultCount)
}

func nextInt(sc *bufio.Scanner) (int, error) {
	sc.Scan()
	return strconv.Atoi(sc.Text())
}

Python: # coding=utf-8


def calc_cost():
    global M
    for diff in range(0, n):
        for i in range(1, n-diff+1):
            j = i + diff
            if diff == 0:
                M[i][j] = 0
            elif diff == 1:
                M[i][j] = p[i-1] * p[i] * p[j]
            else:
                M[i][j] = calc_min(i, j)
            ### debugger ###
            #for row in M:
            #    print(*row)
    return M[1][n]

def calc_min(i, j):
    global M, p
    min_cost = float("inf")
    for k in range(i, j):
        cost = M[i][k] + M[k+1][j] + (p[i-1] * p[k] * p[j])
        #print("i, j, k, cost:", i, j, k, cost)
        if cost < min_cost:
            min_cost = cost
    return min_cost

n = int(input())
M = [["-" for _ in range(n+1)] for _ in range(n+1)]
r_list = []
c_list = []
p = []
for _ in range(n):
    r, c = map(int, input().split())
    r_list.append(r)
    c_list.append(c)

p.append(r_list[0])
for c in c_list:
    p.append(c)

total_cost = calc_cost()
print(total_cost)

Python: import math

n = int(input())
dp = [[0 for j in range(n+1)] for i in range(n+1)]
p = []

def init(p):
    global dp
    for i in range(1, n):
        dp[i][i+1] = p[i-1]*p[i]*p[i+1]

def solve(r, c):
    global dp
    smallest = math.inf
    for i in range(r, c):
        temp = p[r-1]*p[i]*p[c] + dp[r][i] + dp[i+1][c]
        if temp < smallest:
            smallest = temp
    return smallest

first = list(map(int,input().split()))
p.append(first[0])
p.append(first[1])
for i in range(n-1):
    temp = list(map(int,input().split()))
    p.append(temp[1])

init(p)
for j in range(3, n+1):
    for i in range(j-2, 0, -1):
        dp[i][j] = solve(i, j)
print(str(dp[1][n]))

Python: def main():
    n = int(input())
    p = [0] * (n + 1)
    m = [[0] * (n + 1) for _ in range(n + 1)]
    for i in range(n):
        p[i], p[i + 1] = map(int, input().split())
    for l in range(2, n + 1):
        for i in range(1, n - l + 2):
            j = i + l - 1
            m[i][j] = float('inf')
            for k in range(i, j):
                p_prod = p[i - 1] * p[k] * p[j]
                m[i][j] = min(m[i][j], m[i][k] + m[k + 1][j] + p_prod)
    print(m[1][n])


if __name__ == '__main__':
    main()

Python: def matrixChainMultiplication():
	INF = 100 * 100 * 100 + 1
	m = [[INF for _ in range(n + 1)] for _ in range(n + 1)]
	for i in range(1, n + 1):
		m[i][i] = 0
	for l in range(2, n + 1):
		for i in range(1, n - l + 2):
			j = i + l - 1
			for k in range(i, j):
				q = m[i][k] + m[k+1][j] + p[i-1] * p[k] * p[j]
				if q < m[i][j]:
					m[i][j] = q
	return m[1][n]

if __name__ == "__main__":
	n = int(input())
	p = list(map(int, input().split()))
	for i in range(1, n):
		p.append(list(map(int, input().split()))[1])
	print(matrixChainMultiplication())

Python: INF = float("inf")
n = int(input())
A = [0]
for i, a in enumerate(sys.stdin):
    a = list(map(int, a.split()))
    if i == 0:
        A.extend(a)
    else:
        A.append(a[1])
C = [[INF] * (n + 1) for _ in range(n + 1)]
for i in range(n + 1):
    C[i][i] = 0
for j in range(1, n):
    for i in range(1, n - j + 1):
        for k in range(i + 1, i + j + 1):
            cost = C[i][k - 1] + C[k][i + j] + A[i] * A[k] * A[i + j + 1]
            if cost < C[i][i + j]:
                C[i][i + j] = cost
print(C[1][n])

Python: 
from functools import lru_cache


@lru_cache(maxsize=None)
def multi(ms):
    """Matix multiplication. returns minimum number of scalar
    multiplications to compute all matrix multiplications.

    >>> multi(((30, 35), (35, 15), (15, 5), (5, 10), (10, 20), (20, 25)))
    15125
    """
    size = len(ms)
    if size <= 1:
        return 0
    elif size == 2:
        r1, c1 = ms[0]
        r2, c2 = ms[1]
        return c1 * r1 * c2

    costs = []
    for i in range(size-1):
        cost = ms[i][1] * ms[0][0] * ms[-1][1]
        costs.append(multi(ms[:i+1]) + multi(ms[i+1:]) + cost)

    return min(costs)


def run():
    n = int(input())
    ms = []

    for i in range(n):
        m = tuple([int(x) for x in input().split()])
        ms.append(m)

    print(multi(tuple(ms)))


if __name__ == '__main__':
    run()

Python: import sys
n=int(input())+1
e=list(map(lambda x:x.split(),sys.stdin))
p=[int(e[0][0])]+list(int(x[1])for x in e)
m=[[0]*n for _ in[0]*n]
for l in range(2,n):
 for i in range(1,n-l+1):
  j=i+l-1;m[i][j]=1e6
  for k in range(i,j):m[i][j]=min(m[i][j],m[i][k]+m[k+1][j]+p[i-1]*p[k]*p[j])
print(m[1][n-1])

Python: import sys
INF = float("inf")
n = int(input())
A = [0]
for i, a in enumerate(sys.stdin):
    a = list(map(int, a.split()))
    if i == 0:
        A.extend(a)
    else:
        A.append(a[1])
C = [[INF] * (n + 1) for _ in range(n + 1)]
for i in range(n + 1):
    C[i][i] = 0
for j in range(1, n):
    for i in range(1, n - j + 1):
        for k in range(i + 1, i + j + 1):
            cost = C[i][k - 1] + C[k][i + j] + A[i] * A[k] * A[i + j + 1]
            if cost < C[i][i + j]:
                C[i][i + j] = cost
print(C[1][n])

Python: # Solution to this problem using memoization
# https://onlinejudge.u-aizu.ac.jp/problems/ALDS1_10_B

import math

# Optimal cost of multiplying A_1 A_2 ... A_j
def cost(i, j):
    if (i, j) in cost_dict.keys():
        return cost_dict[(i, j)]
    elif i == j:
        cost_dict[(i, j)] = 0
    else:
        mincost = math.inf
        for k in range(i, j):
            estimate = cost(i, k) + cost(k + 1, j) + dims[i - 1] * dims[k] * dims[j]
            mincost = min(estimate, mincost)
        cost_dict[(i, j)] = mincost
    return cost_dict[(i, j)]


if __name__ == '__main__':
    # Number of matrices
    n = int(input())
    # A dictionary to store costs
    cost_dict = dict()
    # Take input dimensions
    dims = []
    for _ in range(n - 1):
        first = int(input().split()[0])
        dims.append(first)
    dims.extend(list(map(int, input().split())))
    # Calculate the optimal cost
    print(cost(1, n))