A Simple Math Problem

A Simple Math Problem

Time Limit : 3000/1000ms (Java/Other)   Memory Limit : 32768/32768K (Java/Other)
Total Submission(s) : 24   Accepted Submission(s) : 16
Problem Description
Lele now is thinking about a simple function f(x).

If x < 10 f(x) = x.
If x >= 10 f(x) = a0 * f(x-1) + a1 * f(x-2) + a2 * f(x-3) + …… + a9 * f(x-10);
And ai(0<=i<=9) can only be 0 or 1 .

Now, I will give a0 ~ a9 and two positive integers k and m ,and could you help Lele to caculate f(k)%m.
 

Input
The problem contains mutiple test cases.Please process to the end of file.
In each case, there will be two lines.
In the first line , there are two positive integers k and m. ( k<2*10^9 , m < 10^5 )
In the second line , there are ten integers represent a0 ~ a9.
 

Output
For each case, output f(k) % m in one line.
 

Sample Input
10 9999 1 1 1 1 1 1 1 1 1 1 20 500 1 0 1 0 1 0 1 0 1 0
 

Sample Output
45 104
 

Author
linle
 

Source
2007省赛集训队练习赛(6)_linle专场
 
#include<stdio.h>
#include<string.h>

int A[10][10], B[10][10], M;
void mul(int X[10][10], int Y[10][10])
{
	int Z[10][10];
	int i, j, k;
	memset(Z, 0, sizeof(Z));
	for (i = 0; i < 10; i++)
		for (j = 0; j < 10; j++)
			for (k = 0; k < 10; k++)
				Z[i][j] = (Z[i][j] + X[i][k] * Y[k][j] % M) % M;
	memcpy(X, Z, sizeof(Z));
}
int f(int n)
{
	int i;
	if (0<=n&&n<=9	)
		return n;
	n-=9;
	memset(B, 0, sizeof(B));
	for (i = 0; i < 10; i++)
		B[i][i] = 1;
	while (n)
	{
		if (n & 1)	mul(B, A);
		mul(A, A);
		n >>= 1;
	}
	return B[0][0] * 9 + B[0][1] * 8 + B[0][2] * 7 + B[0][3] * 6 + B[0][4] * 5 + B[0][5] * 4 + B[0][6] * 3 + B[0][7] * 2 + B[0][8] * 1 + B[0][9] * 0;
}

int main()
{
	int i, k, b, n, sum,j;
	while (scanf("%d%d", &k, &M) != EOF)
	{
		for (i = 0; i < 10;i++)
		scanf("%d", &A[0][i]);
		for (i = 1; i < 10; i++)
			for (j = 0; j < 10; j++)
			{
				if (j == i - 1)
					A[i][j] = 1;
				else
					A[i][j] = 0;
			}
		printf("%d\n", f(k)%M);
	}
	return 0;
}


请帮我分析解释一下:Searching to scale. To consider both salient and non-salient weights, we choose to automatically search for an optimal (per input channel) scaling factor that minimizes the output difference after quantization for a certain layer. This scaling factor should minimize the difference in output that occurs when quantizing the weights for a given layer, helping to maintain accuracy after quantization. Since the quantization function is not differentiable, we are not able to directly optimize the problem with vanilla backpropagation. There are some techniques relying on approximated gradients, which we found still suffer from unstable convergence. To make the process more stable, we define a search space for the optimal scale by analyzing the factors that will affect the choice of scaling factor. As shown in the last section, the saliency of weight channels is actually determined by the activation scale. Therefore, we simply use a very simple search space: s = sα X, α* = argminα L(sα X ) sX is the average magnitude of activation (per-channel), and we use a single hyperparameter α to balance between the protection of salient and non-salient channels. We can find the best α by a fast grid search over the interval of [0, 1]. We further apply weight clipping to minimize the MSE error of quantization. One of the key advantages of AWQ is its simplicity and efficiency. Unlike methods that rely on back-propagation or complex reconstruction processes, AWQ does not require fine-tuning or extensive calibration data. This makes it particularly well-suited for quantizing large pre-trained models, including instruction-tuned LMs and multi-modal LMs.
最新发布
07-22
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