N - Select Mul(next_permutation)

N - Select Mul
思路:当成字符串输入,先用C++的next_permutation函数找出这个它的全排列,对于每个全排列用暴力分成两段求积(用stoi函数把字符串转成10进制),找出最大的即可

代码:

//https://vjudge.net/contest/485999#problem/N
//C++全排列函数  next_permutation
//枚举n位数的全排列
//对每个全排列暴力分成两端求乘积,找出最大值即可
//stoi函数将字符串转成整数输出
#include<bits/stdc++.h>
using namespace std;
int ans;
string N;
int main() {
	cin >> N;
	sort(N.begin(), N.end());
	do {
		for (int i = 1; i < N.size(); i++) {
			string l = "", r = "";
			for (int j = 0; j < i; j++) l += N[j];
			for (int j = i; j < N.size(); j++) r += N[j];
			if (l[0] == '0' || r[0] == '0') continue;
			ans = max(ans, stoi(l) * stoi(r));
		}
	} while (next_permutation(N.begin(), N.end()));
     cout<<ans<<endl;
}
T32ReturnValueWarp t32_mul_int8_param_init( std::string node_name, std::vectorstd::string idfmts, std::vectorstd::string idtypes, std::vectorstd::string odfmts, std::vectorstd::string odtypes, std::vectorstd::string input_names, std::string kernel_type, int offset, int ctrl_flag, int quantize_type, int output_chn, std::vector<int> in_offset, std::vector<float> in_scale, int out_scale_int, int broad_cast_type, int broad_cast_aligned, std::vector<std::vector<int>> input_shapes, std::vector<std::vector<int>> output_shapes,int quantized,int alpha, std::vector<int16_t> ih_start, std::vector<int16_t> ih_offset, std::vector<int16_t> oh_start, std::vector<int16_t> oh_offset, std::vector<int> set_input_in_oram, std::vector<int> set_output_in_oram) { std::stringstream select_code_str; select_code_str << “t32_mul_int8_param_init(” << dump_data(node_name) << dump_vector(idfmts) << dump_vector(idtypes) << dump_vector(odfmts) << dump_vector(odtypes) << dump_vector(input_names) << dump_data(kernel_type) << dump_data(offset) << dump_data(ctrl_flag) << dump_data(quantize_type) << dump_data(output_chn) << dump_vector(in_offset) << dump_vector(in_scale) << dump_data(out_scale_int) << dump_data(broad_cast_type) << dump_data(broad_cast_aligned) << dump_shape(input_shapes) << dump_shape(output_shapes) <<dump_data(quantized)<<dump_data(alpha)<< dump_vector(ih_start) << dump_vector(ih_offset) << dump_vector(oh_start) << dump_vector(oh_offset) << dump_vector(set_input_in_oram) << dump_vector(set_output_in_oram) << “)”; std::cout<<select_code_str.str()<<std::endl; std::vector<TensorT *> inputs; for (int i = 0; i < int(idfmts.size()); i++) { auto tensor = generate_tensor_t(idfmts[i], idtypes[i], input_shapes[i], set_input_in_oram[i]); inputs.push_back(tensor); } // op output std::vector<TensorT *> outputs; for (int i = 0; i < int(odfmts.size()); i++) { auto tensor = generate_tensor_t(odfmts[i], odtypes[i], output_shapes[i], set_output_in_oram[i]); outputs.push_back(tensor); } // op param MulOpParam base_param; auto mul_par = &base_param; mul_par->broad_cast_aligned = broad_cast_aligned; mul_par->quantized = quantized; mul_par->quantize_type = QuantizationType(quantize_type); mul_par->offset = offset; mul_par->ctrl_flag = ctrl_flag; mul_par->alpha = alpha; mul_par->out_scale = out_scale_int; mul_par->broad_cast_type = BroadCastType(broad_cast_type); mul_par->broad_cast_aligned = broad_cast_aligned; mul_par->in_scale_num = in_scale.size(); for (int i = 0; i < mul_par->in_scale_num; i++) { mul_par->in_scale[i] = in_scale[i]; } mul_par->in_offset_num = in_offset.size(); for (int i = 0; i < mul_par->in_offset_num; i++) { mul_par->in_offset[i] = in_offset[i]; }
最新发布
03-20
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