1096. Consecutive Factors (20)[数学逻辑]

本文介绍了一道PAT-A级编程题的解决方法,题目要求找出给定整数的最大连续因子序列。通过遍历并利用开方优化技巧,实现了高效的求解算法,并附带完整代码实现。

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1. 原题: https://www.patest.cn/contests/pat-a-practise/1096

2. 思路:

题意:找出一个数的最大连续的因子数。数学逻辑题
注意,因子不一定是素数,只要连续即可。
思路:
最容易想到的方法就是穷举法了。可以优化的是因子的最大值。
因子最大值不会超过数的开方,然后遍历即可。
已AC。

3. 源码:

#include <iostream>
#include <algorithm>//使用sqrt函数
using namespace std;

int main(void)
{
	int N;
	int maxCount = 0;//最大个数
	int firstFactor;//结果的第一个起始因子
	cin >> N;

	int nlimit = sqrt((double)N);
	for (int i = 2; i <= nlimit; i++)//遍历
	{
		int tem = N;
		int start = i;
		while (tem % start == 0)
		{
			tem /= start;
			start++;
		}

		if (start - i > maxCount)//更新最大值
		{
			maxCount = start - i;
			firstFactor = i;
		}
	}

	if (maxCount == 0)//说明是素数,输出它本身
	{
		cout << "1\n" << N << endl;
	}
	else
	{
		cout << maxCount << endl;
		for (int i = 0; i < maxCount; i++)
		{
			if (i != 0)
				cout << '*';
			cout << firstFactor++;
		}
		cout << endl;
	}

	return 0;
}


解释代码内容: def run_backend(cfg, model, states, keyframes, K): set_global_config(cfg) device = keyframes.device factor_graph = FactorGraph(model, keyframes, K, device) retrieval_database = load_retriever(model) mode = states.get_mode() while mode is not Mode.TERMINATED: mode = states.get_mode() if mode == Mode.INIT or states.is_paused(): time.sleep(0.01) continue if mode == Mode.RELOC: frame = states.get_frame() success = relocalization(frame, keyframes, factor_graph, retrieval_database) if success: states.set_mode(Mode.TRACKING) states.dequeue_reloc() continue idx = -1 with states.lock: if len(states.global_optimizer_tasks) > 0: idx = states.global_optimizer_tasks[0] if idx == -1: time.sleep(0.01) continue # Graph Construction kf_idx = [] # k to previous consecutive keyframes n_consec = 1 for j in range(min(n_consec, idx)): kf_idx.append(idx - 1 - j) frame = keyframes[idx] retrieval_inds = retrieval_database.update( frame, add_after_query=True, k=config["retrieval"]["k"], min_thresh=config["retrieval"]["min_thresh"], ) kf_idx += retrieval_inds lc_inds = set(retrieval_inds) lc_inds.discard(idx - 1) if len(lc_inds) > 0: print("Database retrieval", idx, ": ", lc_inds) kf_idx = set(kf_idx) # Remove duplicates by using set kf_idx.discard(idx) # Remove current kf idx if included kf_idx = list(kf_idx) # convert to list frame_idx = [idx] * len(kf_idx) if kf_idx: factor_graph.add_factors( kf_idx, frame_idx, config["local_opt"]["min_match_frac"] ) with states.lock: states.edges_ii[:] = factor_graph.ii.cpu().tolist() states.edges_jj[:] = factor_graph.jj.cpu().tolist() if config["use_calib"]: factor_graph.solve_GN_calib() else: factor_graph.solve_GN_rays() with states.lock: if len(states.global_optimizer_tasks) > 0: idx = states.global_optimizer_tasks.pop(0)
03-17
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