**Evaluating time complexity**

Some problems can be solved in many ways with different complexity.

For example, an array of n numbers can be sorted in O(n²) steps - (in a naive way) or in O(n*logn) steps (in an optimal way).

For 1000 numbers, naive sorting will require about 1000000 steps, whereas optimal sorting will need only 10000 - that's 100 times less.

For sufficiently big sets of input data our system can help to determine the complexity of the algorithm, based on execution time.

**Example challenge**

We call the number 1+2+...+k the k-th triangular number and denote it T_k. The input of the program contains a certain number of positive integers. For each integer n provided at input, the program should compute and print to output the sum of the first n triangular numbers, T_1 + T_2 + ... + T_n.

For instance, for n=3 we have:

T_1 = 1

T_2 = 1 + 2

T_3 = 1 + 2 + 3

T_1 + T_2 + T_3 = 10

**Example input:**

1

3

10

**Example output**

1

10

220