2016 Multi-University Training Contest 2 Acperience
Acperience
Time Limit: 4000/2000 MS (Java/Others) Memory Limit: 65536/65536 K (Java/Others)Total Submission(s): 730 Accepted Submission(s): 188
Problem Description
Deep neural networks (DNN) have shown significant improvements in several application domains including computer vision and speech recognition. In computer vision, a particular type of DNN, known as Convolutional Neural Networks (CNN), have demonstrated state-of-the-art results in object recognition and detection.
Convolutional neural networks show reliable results on object recognition and detection that are useful in real world applications. Concurrent to the recent progress in recognition, interesting advancements have been happening in virtual reality (VR by Oculus), augmented reality (AR by HoloLens), and smart wearable devices. Putting these two pieces together, we argue that it is the right time to equip smart portable devices with the power of state-of-the-art recognition systems. However, CNN-based recognition systems need large amounts of memory and computational power. While they perform well on expensive, GPU-based machines, they are often unsuitable for smaller devices like cell phones and embedded electronics.
In order to simplify the networks, Professor Zhang tries to introduce simple, efficient, and accurate approximations to CNNs by binarizing the weights. Professor Zhang needs your help.
More specifically, you are given a weighted vector W=(w1,w2,...,wn). Professor Zhang would like to find a binary vector B=(b1,b2,...,bn) (bi∈{+1,−1})and a scaling factor α≥0 in such a manner that ∥W−αB∥ 2 is minimum.
Note that ∥⋅∥ denotes the Euclidean norm (i.e. ∥X∥=x21+⋯+x2n−−−−−−−−−−√, where X=(x1,x2,...,xn)).
Convolutional neural networks show reliable results on object recognition and detection that are useful in real world applications. Concurrent to the recent progress in recognition, interesting advancements have been happening in virtual reality (VR by Oculus), augmented reality (AR by HoloLens), and smart wearable devices. Putting these two pieces together, we argue that it is the right time to equip smart portable devices with the power of state-of-the-art recognition systems. However, CNN-based recognition systems need large amounts of memory and computational power. While they perform well on expensive, GPU-based machines, they are often unsuitable for smaller devices like cell phones and embedded electronics.
In order to simplify the networks, Professor Zhang tries to introduce simple, efficient, and accurate approximations to CNNs by binarizing the weights. Professor Zhang needs your help.
More specifically, you are given a weighted vector W=(w1,w2,...,wn). Professor Zhang would like to find a binary vector B=(b1,b2,...,bn) (bi∈{+1,−1})and a scaling factor α≥0 in such a manner that ∥W−αB∥ 2 is minimum.
Note that ∥⋅∥ denotes the Euclidean norm (i.e. ∥X∥=x21+⋯+x2n−−−−−−−−−−√, where X=(x1,x2,...,xn)).
Input
There are multiple test cases. The first line of input contains an integer
T, indicating the number of test cases. For each test case:
The first line contains an integers n (1≤n≤100000) -- the length of the vector. The next line contains n integers: w1,w2,...,wn (−10000≤wi≤10000).
The first line contains an integers n (1≤n≤100000) -- the length of the vector. The next line contains n integers: w1,w2,...,wn (−10000≤wi≤10000).
Output
For each test case, output the minimum value of
∥W−αB∥2 as an irreducible fraction "
p/
q" where
p,
q are integers,
q>0.
Sample Input
3 4 1 2 3 4 4 2 2 2 2 5 5 6 2 3 4
Sample Output
5/1 0/1 10/1
思路：
本题要求∥W−αB∥^2的最小值，而根据题目中所说的对//。//定义，也就是求（W1-α*B)^2+（W2-α*B)^2+（W3-α*B)^2
+……+（Wn-α*B)^2的最小值，对于α>=0且是未知的,可以通过变换B让每一项最小，当Wi为正的时候，取B=1，
（Wi-α）^2，反之，取-1,（Wi+α）^2=（-Wi-α）^2；可归结为（|Wi|-α）^2。这是一个求方差公式，当α取平均
值时，可使值最小。
AC代码：
对于（|Wi|-α）^2展开得（Wi）^2-2*|Wi|*α+α^2;
#include
#include
#include
#include
#include
#include
using namespace std;
#define INF 999999999
__int64 gcd(__int64 a,__int64 b)//求最大公约数
{
__int64 k,temp;
if(a suma+=abs(x[i]*x[i]);
}
__int64 sumcz=sum/gcd(sum,n)*sum;
__int64 sumcm=n/gcd(sum,n);
sumcz=suma*sumcm-sumcz;
printf("%I64d/%I64d\n",(sumcz/gcd(sumcz,sumcm)),sumcm/gcd(sumcz,sumcm));
}
return 0;
}
错误代码：（由于中间会出现数值太大，爆内存情况）
#include
#include
#include
#include
#include
#include
using namespace std;
#define INF 999999999
__int64 gcd(__int64 a,__int64 b)
{
__int64 k,temp;
if(a