HDU 3828 A+B problem

A+B Problem II——大数定理 A+B Problem II 时间限制:3000 ms  |  内存限制:65535 KB 难度:3 描述 I have a very simple problem for you. Given two integers A and B, your job is to calculate the Sum of A + B. A,B must be positive. 输入 阅读详情
Problem Description
QQ and OO always play the game of A + B. QQ gives tow decimal number A and B, and OO answers the sum at once. But to do the same things too often is boring. So, today they are fed up with the easy game, and they come up with new rules of the game.
Rule1: When add A and B we use the binary system string of A and B.
Rule2: We can overlap the same suffix of A and prefix of B.
Rule3: If the binary system string of B is a substring of A we can use A to overlap B.

245       11110101

351               101011111

107             1101011

+-------------------------

 3935     111101011111

To make the problem more interesting, QQ gives n numbers, OO should use every one of them and every one once, then give the smallest of the sum. Now OO have no time to do it and need your help. You're a talented programmer you can do it.


Input
There are several tests, every test begin with n followed with n (0 < n < 16) lines, each line is a decimal number ai(0 < ai < 2^64) as described above.
Process until EOF.


Output
For each test you should print the smallest answer per line, maybe the answer is large you should mod 1000000009.


Sample Input
2
5
3
3
245
351
107


Sample Output
11

3935


#include <stdio.h>
#include <stdlib.h>
#include <string.h>

int n;
int lena[16];
char a[16][65];
int f[1 << 16][16]; // f[i][j] 状态i,以j为开头时,串最小长
int match[16][16];   //k = match[i][j], a[i][len - k..0] == a[j]

[len..k]

int DtoB(long long d, char *b){
	int i;
	for (i = 0; d > 0; i++){
		b[i] = d & 1;
		d = d >> 1;
	}
	return i;
}

int Cmp(int p, int q, int pre, int stat){
	int i, j;
	i = f[stat][p];
	j = f[stat][q];
	if (pre >= 0){
		i = i - match[pre][p];
		j = j - match[pre][q];
	}
	if (i > j) return 1;
	if (i < j) return -1;
	
	i = lena[p] - 1;
	j = lena[q] - 1;
	if (pre >= 0){
		i = i - match[pre][p];
		j = j - match[pre][q];
	}
	for (; i >= 0 && j >= 0; i--, j--)
		if (a[p][i] != a[q][j]) break;
	if (i < 0 || j < 0) return 0;
	if (a[p][i] > a[q][j]) return 1;
	else return -1;
}

void CalcMatch(){
	int i, j, p, q;
	for (i = 0; i < n; i++)
		for (j = 0; j < n; j++){
			if (i == j) continue;
			for (p = lena[i] - 1; p >= 0; p--){
				for (q = lena[j] - 1; q >= 0 && p - 

(lena[j] - 1 - q) >= 0; q--)
					if (a[i][p - (lena[j] - 1 - q)] 

!= a[j][q]) break;
				if (p - (lena[j] - 1 - q) < 0 || q < 0) 

break;
			}
			match[i][j] = lena[j] - q - 1;
		}
}

int main(){
	int i, j, k, i1, lent, pre, fstat;
	long long t;
	char ans[1024];
	
//	freopen("A+B.in","r",stdin);
//	freopen("out.txt","w",stdout);
	while(scanf("%d", &n) != EOF){
		fstat = (1 << n) - 1;
		for (i = 0; i < n; i++){
			scanf("%I64d", &t);
			lena[i] = DtoB(t, a[i]);
		}
		CalcMatch();
		memset(f[0], 0, sizeof(f[0]));
		
		for (i = 1; i <= fstat; i++){
			for (k = 0; k < n; k++){
				if (((1 << k) & i) == 0) continue;
				i1 = i - (1 << k);
				if (i1 == 0){
					f[i][k] = lena[k];
					continue;
				}
				f[i][k] = 0x7fffffff;
				for (j = 0; j < n; j++){
					if (((1 << j) & i1) == 0) 

continue;
					if (f[i][k] > f[i1][j] +  lena

[k] - match[k][j])
						f[i][k] = f[i1][j] + 

lena[k] - match[k][j];
				}
			}
		}

		pre = -1;
		lent = 0;
		while(fstat){
			k = -1;
			for (i = 0; i < n; i++){
				if (((1 << i) & fstat) == 0) continue;
				if (k < 0 || Cmp(i, k, pre, fstat) <= 

0)
					k = i;
			}

			fstat = fstat - (1 << k);
			if (pre >= 0) i = lena[k] - 1 - match[pre][k];
			else i = lena[k] - 1;
			if (i >= 0) pre = k;
			for (; i >= 0; i--, lent++)
				ans[lent] = a[k][i];
		}
		
		t = 0;
		for (i = 0; i < lent; i++){
			t = (t * 2 + ans[i]) % 1000000009;
		}
		printf("%I64d\n", t);
	}
	return 0;
}

/****************
一看到n<16,就想到TSP问题,集合DP
f[i][j] 状态i,以j为开头时,串最小长
先把最小串长求出来,之后再把串搜出来。
由于是以j为开头,所以当两个状态有相同长度时,取串j字典序小的
这样保证了得到结果最小。
之所以不直接记录串本身,只记录串长,是觉得貌似不满足最优子结构性质,即

原串字典序最小,子串字典序不一定也最小的感觉。。。其实也看怎么设计了。

。。反正这么写过一次WA了。。
注意,相交可以预处理出来,集合要反复用时,最好也先预处理出来
*****************/

C语言之A + B problem A + B problem欢迎进入我的C语言世界题目答案本题感悟对scanf("%d%d",&a,&b) != EOF的理解 欢迎进入我的C语言世界 题目 Problem Description Calculate a + b. Input The input will consist of a series of pairs of integers a and b,separat... 阅读详情

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Download.2. addpath(genpath('DeepLearnToolbox'));Example: Deep Belief Network---------------------```matlabfunction test_example_DBNload mnist_uint8;train_x = double(train_x) / 255;test_x = double(test_x) / 255;train_y = double(train_y);test_y = double(test_y);%% ex1 train a 100 hidden unit RBM and visualize its weightsrand('state',0)dbn.sizes = [100];opts.numepochs = 1;opts.batchsize = 100;opts.momentum = 0;opts.alpha = 1;dbn = dbnsetup(dbn, train_x, opts);dbn = dbntrain(dbn, train_x, opts);figure; visualize(dbn.rbm{1}.W'); % Visualize the RBM weights%% ex2 train a 100-100 hidden unit DBN and use its weights to initialize a NNrand('state',0)%train dbndbn.sizes = [100 100];opts.numepochs = 1;opts.batchsize = 100;opts.momentum = 0;opts.alpha = 1;dbn = dbnsetup(dbn, train_x, opts);dbn = dbntrain(dbn, train_x, opts);%unfold dbn to nnnn = dbnunfoldtonn(dbn, 10);nn.activation_function = 'sigm';%train nnopts.numepochs = 1;opts.batchsize = 100;nn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.10, 'Too big error');```Example: Stacked Auto-Encoders---------------------```matlabfunction test_example_SAEload mnist_uint8;train_x = double(train_x)/255;test_x = double(test_x)/255;train_y = double(train_y);test_y = double(test_y);%% ex1 train a 100 hidden unit SDAE and use it to initialize a FFNN% Setup and train a stacked denoising autoencoder (SDAE)rand('state',0)sae = saesetup([784 100]);sae.ae{1}.activation_function = 'sigm';sae.ae{1}.learningRate = 1;sae.ae{1}.inputZeroMaskedFraction = 0.5;opts.numepochs = 1;opts.batchsize = 100;sae = saetrain(sae, train_x, opts);visualize(sae.ae{1}.W{1}(:,2:end)')% Use the SDAE to initialize a FFNNnn = nnsetup([784 100 10]);nn.activation_function = 'sigm';nn.learningRate = 1;nn.W{1} = sae.ae{1}.W{1};% Train the FFNNopts.numepochs = 1;opts.batchsize = 100;nn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.16, 'Too big error');```Example: Convolutional Neural Nets---------------------```matlabfunction test_example_CNNload mnist_uint8;train_x = double(reshape(train_x',28,28,60000))/255;test_x = double(reshape(test_x',28,28,10000))/255;train_y = double(train_y');test_y = double(test_y');%% ex1 Train a 6c-2s-12c-2s Convolutional neural network %will run 1 epoch in about 200 second and get around 11% error. %With 100 epochs you'll get around 1.2% errorrand('state',0)cnn.layers = { struct('type', 'i') %input layer struct('type', 'c', 'outputmaps', 6, 'kernelsize', 5) %convolution layer struct('type', 's', 'scale', 2) %sub sampling layer struct('type', 'c', 'outputmaps', 12, 'kernelsize', 5) %convolution layer struct('type', 's', 'scale', 2) %subsampling layer};cnn = cnnsetup(cnn, train_x, train_y);opts.alpha = 1;opts.batchsize = 50;opts.numepochs = 1;cnn = cnntrain(cnn, train_x, train_y, opts);[er, bad] = cnntest(cnn, test_x, test_y);%plot mean squared errorfigure; plot(cnn.rL);assert(er<0.12, 'Too big error');```Example: Neural Networks---------------------```matlabfunction test_example_NNload mnist_uint8;train_x = double(train_x) / 255;test_x = double(test_x) / 255;train_y = double(train_y);test_y = double(test_y);% normalize[train_x, mu, sigma] = zscore(train_x);test_x = normalize(test_x, mu, sigma);%% ex1 vanilla neural netrand('state',0)nn = nnsetup([784 100 10]);opts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samples[nn, L] = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.08, 'Too big error');%% ex2 neural net with L2 weight decayrand('state',0)nn = nnsetup([784 100 10]);nn.weightPenaltyL2 = 1e-4; % L2 weight decayopts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samplesnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex3 neural net with dropoutrand('state',0)nn = nnsetup([784 100 10]);nn.dropoutFraction = 0.5; % Dropout fraction opts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samplesnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex4 neural net with sigmoid activation functionrand('state',0)nn = nnsetup([784 100 10]);nn.activation_function = 'sigm'; % Sigmoid activation functionnn.learningRate = 1; % Sigm require a lower learning rateopts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samplesnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex5 plotting functionalityrand('state',0)nn = nnsetup([784 20 10]);opts.numepochs = 5; % Number of full sweeps through datann.output = 'softmax'; % use softmax outputopts.batchsize = 1000; % Take a mean gradient step over this many samplesopts.plot = 1; % enable plottingnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex6 neural net with sigmoid activation and plotting of validation and training error% split training data into training and validation datavx = train_x(1:10000,:);tx = train_x(10001:end,:);vy = train_y(1:10000,:);ty = train_y(10001:end,:);rand('state',0)nn = nnsetup([784 20 10]); nn.output = 'softmax'; % use softmax outputopts.numepochs = 5; % Number of full sweeps through dataopts.batchsize = 1000; % Take a mean gradient step over this many samplesopts.plot = 1; % enable plottingnn = nntrain(nn, tx, ty, opts, vx, vy); % nntrain takes validation set as last two arguments (optionally)[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');```[![Bitdeli Badge](https://d2weczhvl823v0.cloudfront.net/rasmusbergpalm/deeplearntoolbox/trend.png)](https://bitdeli.com/free "Bitdeli Badge")

POJ 3252 Round Number

Round Numbers Time Limit: 2000MS Memory Limit: 65536K Total Submissions: 5077 Accepted: 1736 Description The cows, as you know, have no fingers or thumbs and thus are unable to play Scisso

cmonkey 1488

POJ 2430 Lazy Cows

Lazy Cows Time Limit: 1000MS   Memory Limit: 65536K Total Submissions: 1897   Accepted: 718 Description Farmer John regrets having applied high-grade fertilizer to hi

cmonkey 1446

HDU 3880 luck number

#include #include #include int main(){ __int64 i, j, k; __int64 a, b, M; while(scanf("%I64d%I64d%I64d", &a, &b, &M) != EOF){ if (a > b){ k = a; a = b; b = k; } if (b == 1){ k

cmonkey 1436

POJ 1671 Rhyme Schemes

Rhyme Schemes Time Limit: 1000MS   Memory Limit: 10000K Total Submissions: 1175   Accepted: 645   Special Judge Description The rhyme scheme for a poem (or stanza o

cmonkey 1335

HDU 3864 D_num

Problem Description Oregon Maple was waiting for Bob When Bob go back home. Oregon Maple asks Bob a problem that as a Positive number N, if there are only four Positive number M makes Gcd(N, M) == M

cmonkey 1202

HDU 3652 B-number

题意: 1..n的数中数字包含‘13’且能被13整除的,有多少个。n 分析: 按位DP 困在能被13整除上了,查了能被13整除的数的特性,不知道怎么转化dp... 后来看了解题报告,状态上多记录个模13的余数就好了~ #include #include #include int ans; int num[10], ln; int f[10][13][3]; void

cmonkey 1084

POJ 1037 A decorative fence

A decorative fence Time Limit: 1000MS   Memory Limit: 10000K Total Submissions: 5280   Accepted: 1880 Description Richard just finished building his new house. Now the

cmonkey 1062

2014 微软 编程之美 初赛 第一场 题解

2014 微软 编程之美 初赛 第一场 题解 趁热火写个题解.这场一共才三题. 第一题 焦距 这...大致就是给你公式照着敲,处理好字符串就行,自认为代码写的蛮干净的~~ 第二题 树 这是最难的一题,但是大数据太弱了,竟然都暴过了... 题意:N(1e5)个点的树,初始点权都为0,有Q(1e5)个操作,每次操作在以u为根的子树中,对深度(根的深度为1)在区间[l,r]内

cmonkey 1062

HDU 3831 DICS

Problem Description This time, we are involved in a more complicated one: given two strings SOURCE and DESTINATION , we have 4 operations Delete: remove the ith character. Insert: in pos i(the ith

cmonkey 1039
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