1096 Consecutive Factors (20分)

这是一个逻辑题,实际上就是双重循环遍历就可以,很容易可以想到因子的最大值为根号N,然后什么情况下会使得连乘为构成N的因子呢,其实很显然,那便是连乘的结果为 N的因子(即N%res==0),这样只要双重循环,外层控制开始元素,不断相乘然后判断结果是否为因子就可以了。

附本人AC代码:

#include<iostream>
#include<vector>
#include<math.h>
using namespace std;
int main() {
	int N, t, first = 0, maxlen = 0;
	scanf("%d", &N);
	t = sqrt(N);
	for (int i = 2; i <= t; i++) {
		int tmp = 1, j = i;
		for (; j <= t; j++) {
			tmp *= j;
			if (N%tmp != 0)break;
		}
		if (j - i > maxlen) {
			maxlen = j - i;
			first = i;
		}
	}
	if (first == 0)printf("1\n%d", N);
	else {
		printf("%d\n", maxlen);
		for (int i = first; i < first + maxlen; i++) {
			if (i == first)printf("%d", i);
			else printf("*%d", i);
		}
	}
	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)
最新发布
03-17
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