1096. Consecutive Factors (20)

本文介绍了一个算法,用于找出一个整数的最大连续因子序列。通过使用平方根作为上限进行优化,该算法能有效地找到最长的连续因子序列,并展示了解决方案的C语言实现。

摘要生成于 C知道 ,由 DeepSeek-R1 满血版支持, 前往体验 >

1096. Consecutive Factors (20)

#include <stdio.h>
#include <math.h>
int main()
{
    int n;
    scanf("%d",&n);
    int key=sqrt((double)n);
    int s,maxlen=0;
    for(int i=2;i<=key;++i)
    {
        int m=n;
        int curlen=0;
        int k=i;
        while(m%k==0)
        {
            ++curlen;
            m/=k;++k;
        }
        if(curlen>maxlen)
        {
            maxlen=curlen;
            s=i;
        }
    }
    if(!maxlen)
    {
        printf("1\n%d",n);return 0;
    }
    printf("%d\n%d",maxlen,s);
    while(--maxlen)
    {
        printf("*%d",++s);
    }
    return 0;
}
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
05-31
评论
添加红包

请填写红包祝福语或标题

红包个数最小为10个

红包金额最低5元

当前余额3.43前往充值 >
需支付:10.00
成就一亿技术人!
领取后你会自动成为博主和红包主的粉丝 规则
hope_wisdom
发出的红包
实付
使用余额支付
点击重新获取
扫码支付
钱包余额 0

抵扣说明:

1.余额是钱包充值的虚拟货币,按照1:1的比例进行支付金额的抵扣。
2.余额无法直接购买下载,可以购买VIP、付费专栏及课程。

余额充值