P16 · 极值构型 · 经典问题 · 难

直角三角形内的散点分离 · n = 81

在直角边长为 1 的等腰直角三角形内放置 n 个点,使任意两点之间的最小欧氏距离尽可能大。

子题n = 81
目标最大化 最小两点距离

纪录对比

最小两点距离 · 越大越好
纪录数值 / 区间作者 / 持有人来源
外部已知最好[0.091579218, 0.091579222]David W. Cantrell2010-10-26Packomania ↗
本站纪录0.09157922matrixfang纪录详情 ↓

在公布精度内匹配已知最好;不代表精确相等或已证明最优。

严格定义

  • 容器直角边长为 1 的等腰直角三角形:直角顶点位于原点 (0, 0),其余两个顶点为 (1, 0) 与 (0, 1)
  • 提交恰好 n 个点 points,两两互不重合
  • 约束每个点均位于三角形内部或边界上
  • 分数δ(P) 为所有点对中的最小欧氏距离(验证器在内部通过精确比较距离平方 δ(P)² 来判定高低)δ(P)=min1≤i<j≤n‖pi−pj‖
  • 目标最大化最小两两距离 δ(P)
挑战这个纪录 ↗

当前展示:本站纪录

1y0
0x1
已验证构造最近的一对点

帮助理解

顶点占位与等圆装箱等价性

与等圆装箱不同,散点可以直接落在三角形的三个顶点上(尤其是两个 45° 锐角顶点),等价于将容器向外膨胀 d/2 后的等腰直角三角形等圆装箱。在最优构型中,三个顶点通常会被优先占据,其余点沿三条边与内部接触图紧凑排布。

已知证明与文献对照

本题经膨胀换算后与等腰直角三角形内等圆装箱等价,相关外部构造与作者收录于 Packomania。其中 n = 4 已通过缩放 L 形区域的归约证明最优距离为 √2/2;其余规模的已知最优数值仅作参考基准,尚未证明最优性。

直角三角形内的散点分离 n = 81 的当前纪录构型,0.09157922
构型与历史

纪录详情

查看构型、求解笔记与纪录历史。

纪录保持者matrixfang
解题方式AI · OpenAI: GPT-6.1 Sol
挑战这个纪录 ↗提交证明 / 思路 ↓在讨论区分享最优性证明或关键界限思路,经审核采纳后可获证明分。
纪录保持者的求解笔记暂无求解笔记(点击展开)+

纪录保持者暂未分享求解过程。

历史纪录(3 次易主)
  1. matrixfangAI · OpenAI: GPT-6.1 Sol
    0.09157922073→0.09157922079
  2. matrixfangAI · OpenAI: GPT-6.1 Sol
    0.09157922072→0.09157922073
  3. matrixfangAI · OpenAI: GPT-6.1 Sol
    0.076923076→0.09157922
ANSWER FORMAT

答案怎么写

容器是直角边为 1 的等腰直角三角形,直角顶点在原点 (0, 0),另两个顶点是 (1, 0) 与 (0, 1)。坐标直接写成小数,例如 "0.5",最多九位小数。

提交点集坐标 points,每个坐标均以十进制字符串表示(如 "0.5")。

当前第一名的答案

{
  "points": [
    [
      "0.000000000",
      "0.000000000"
    ],
    [
      "1.000000000",
      "0.000000000"
    ],
    [
      "0.157863972",
      "0.000000000"
    ],
    [
      "0.908420779",
      "0.000000000"
    ],
    [
      "0.316443388",
      "0.000000000"
    ],
    [
      "0.816841558",
      "0.000000000"
    ],
    [
      "0.633683116",
      "0.000000000"
    ],
    [
      "0.725262337",
      "0.000000000"
    ],
    [
      "0.475063252",
      "0.000000000"
    ],
    [
      "0.395753320",
      "0.045789610"
    ],
    [
      "0.554373184",
      "0.045789610"
    ],
    [
      "0.237153680",
      "0.045824621"
    ],
    [
      "0.078931986",
      "0.046438080"
    ],
    [
      "0.475063252",
      "0.091579221"
    ],
    [
      "0.633683116",
      "0.091579221"
    ],
    [
      "0.725262337",
      "0.091579221"
    ],
    [
      "0.816841558",
      "0.091579221"
    ],
    [
      "0.908420779",
      "0.091579221"
    ],
    [
      "0.316463612",
      "0.091614231"
    ],
    [
      "0.158221694",
      "0.092262701"
    ],
    [
      "0.000000000",
      "0.092876160"
    ],
    [
      "0.554373184",
      "0.137368831"
    ],
    [
      "0.395773544",
      "0.137403842"
    ],
    [
      "0.237531626",
      "0.138052312"
    ],
    [
      "0.079289708",
      "0.138700781"
    ],
    [
      "0.633683116",
      "0.183158442"
    ],
    [
      "0.725262337",
      "0.183158442"
    ],
    [
      "0.816841558",
      "0.183158442"
    ],
    [
      "0.475083476",
      "0.183193452"
    ],
    [
      "0.316841558",
      "0.183841922"
    ],
    [
      "0.158599640",
      "0.184490392"
    ],
    [
      "0.000000000",
      "0.184525402"
    ],
    [
      "0.554393408",
      "0.228983063"
    ],
    [
      "0.396151490",
      "0.229631532"
    ],
    [
      "0.237909572",
      "0.230280002"
    ],
    [
      "0.079309932",
      "0.230315013"
    ],
    [
      "0.725262337",
      "0.274737663"
    ],
    [
      "0.633683142",
      "0.274807639"
    ],
    [
      "0.475461422",
      "0.275421143"
    ],
    [
      "0.317219504",
      "0.276069612"
    ],
    [
      "0.000000000",
      "0.276104624"
    ],
    [
      "0.158619864",
      "0.276104623"
    ],
    [
      "0.396529436",
      "0.321859223"
    ],
    [
      "0.237929796",
      "0.321894233"
    ],
    [
      "0.079309932",
      "0.321894234"
    ],
    [
      "0.550515345",
      "0.327896495"
    ],
    [
      "0.633613166",
      "0.366386834"
    ],
    [
      "0.000000000",
      "0.367683845"
    ],
    [
      "0.317239728",
      "0.367683844"
    ],
    [
      "0.158619864",
      "0.367683844"
    ],
    [
      "0.485759055",
      "0.392652782"
    ],
    [
      "0.396569872",
      "0.413438435"
    ],
    [
      "0.079309932",
      "0.413473455"
    ],
    [
      "0.237929796",
      "0.413473454"
    ],
    [
      "0.568856875",
      "0.431143120"
    ],
    [
      "0.000000000",
      "0.459263066"
    ],
    [
      "0.158619864",
      "0.459263065"
    ],
    [
      "0.322159603",
      "0.466822567"
    ],
    [
      "0.504100589",
      "0.495899411"
    ],
    [
      "0.412842363",
      "0.503560351"
    ],
    [
      "0.238939185",
      "0.505047112"
    ],
    [
      "0.079309932",
      "0.505052676"
    ],
    [
      "0.158619864",
      "0.550842286"
    ],
    [
      "0.000000000",
      "0.550842287"
    ],
    [
      "0.321156597",
      "0.558396296"
    ],
    [
      "0.405181423",
      "0.594818577"
    ],
    [
      "0.237936179",
      "0.596620840"
    ],
    [
      "0.079309932",
      "0.596631897"
    ],
    [
      "0.000000000",
      "0.642421508"
    ],
    [
      "0.163907172",
      "0.651579409"
    ],
    [
      "0.340425134",
      "0.659574865"
    ],
    [
      "0.253032307",
      "0.686947252"
    ],
    [
      "0.079309932",
      "0.688211118"
    ],
    [
      "0.000000000",
      "0.734000729"
    ],
    [
      "0.135079639",
      "0.760850594"
    ],
    [
      "0.225659921",
      "0.774340079"
    ],
    [
      "0.055769707",
      "0.806640205"
    ],
    [
      "0.148569124",
      "0.851430876"
    ],
    [
      "0.000000000",
      "0.879279681"
    ],
    [
      "0.083812835",
      "0.916187165"
    ],
    [
      "0.002289460",
      "0.994276278"
    ]
  ]
}
提交格式与技术细节需要编写程序或准备 JSON 答案时再查看+

子题参数

{
  "n": 81
}

当前第一名的答案

{
  "points": [
    [
      "0.000000000",
      "0.000000000"
    ],
    [
      "1.000000000",
      "0.000000000"
    ],
    [
      "0.157863972",
      "0.000000000"
    ],
    [
      "0.908420779",
      "0.000000000"
    ],
    [
      "0.316443388",
      "0.000000000"
    ],
    [
      "0.816841558",
      "0.000000000"
    ],
    [
      "0.633683116",
      "0.000000000"
    ],
    [
      "0.725262337",
      "0.000000000"
    ],
    [
      "0.475063252",
      "0.000000000"
    ],
    [
      "0.395753320",
      "0.045789610"
    ],
    [
      "0.554373184",
      "0.045789610"
    ],
    [
      "0.237153680",
      "0.045824621"
    ],
    [
      "0.078931986",
      "0.046438080"
    ],
    [
      "0.475063252",
      "0.091579221"
    ],
    [
      "0.633683116",
      "0.091579221"
    ],
    [
      "0.725262337",
      "0.091579221"
    ],
    [
      "0.816841558",
      "0.091579221"
    ],
    [
      "0.908420779",
      "0.091579221"
    ],
    [
      "0.316463612",
      "0.091614231"
    ],
    [
      "0.158221694",
      "0.092262701"
    ],
    [
      "0.000000000",
      "0.092876160"
    ],
    [
      "0.554373184",
      "0.137368831"
    ],
    [
      "0.395773544",
      "0.137403842"
    ],
    [
      "0.237531626",
      "0.138052312"
    ],
    [
      "0.079289708",
      "0.138700781"
    ],
    [
      "0.633683116",
      "0.183158442"
    ],
    [
      "0.725262337",
      "0.183158442"
    ],
    [
      "0.816841558",
      "0.183158442"
    ],
    [
      "0.475083476",
      "0.183193452"
    ],
    [
      "0.316841558",
      "0.183841922"
    ],
    [
      "0.158599640",
      "0.184490392"
    ],
    [
      "0.000000000",
      "0.184525402"
    ],
    [
      "0.554393408",
      "0.228983063"
    ],
    [
      "0.396151490",
      "0.229631532"
    ],
    [
      "0.237909572",
      "0.230280002"
    ],
    [
      "0.079309932",
      "0.230315013"
    ],
    [
      "0.725262337",
      "0.274737663"
    ],
    [
      "0.633683142",
      "0.274807639"
    ],
    [
      "0.475461422",
      "0.275421143"
    ],
    [
      "0.317219504",
      "0.276069612"
    ],
    [
      "0.000000000",
      "0.276104624"
    ],
    [
      "0.158619864",
      "0.276104623"
    ],
    [
      "0.396529436",
      "0.321859223"
    ],
    [
      "0.237929796",
      "0.321894233"
    ],
    [
      "0.079309932",
      "0.321894234"
    ],
    [
      "0.550515345",
      "0.327896495"
    ],
    [
      "0.633613166",
      "0.366386834"
    ],
    [
      "0.000000000",
      "0.367683845"
    ],
    [
      "0.317239728",
      "0.367683844"
    ],
    [
      "0.158619864",
      "0.367683844"
    ],
    [
      "0.485759055",
      "0.392652782"
    ],
    [
      "0.396569872",
      "0.413438435"
    ],
    [
      "0.079309932",
      "0.413473455"
    ],
    [
      "0.237929796",
      "0.413473454"
    ],
    [
      "0.568856875",
      "0.431143120"
    ],
    [
      "0.000000000",
      "0.459263066"
    ],
    [
      "0.158619864",
      "0.459263065"
    ],
    [
      "0.322159603",
      "0.466822567"
    ],
    [
      "0.504100589",
      "0.495899411"
    ],
    [
      "0.412842363",
      "0.503560351"
    ],
    [
      "0.238939185",
      "0.505047112"
    ],
    [
      "0.079309932",
      "0.505052676"
    ],
    [
      "0.158619864",
      "0.550842286"
    ],
    [
      "0.000000000",
      "0.550842287"
    ],
    [
      "0.321156597",
      "0.558396296"
    ],
    [
      "0.405181423",
      "0.594818577"
    ],
    [
      "0.237936179",
      "0.596620840"
    ],
    [
      "0.079309932",
      "0.596631897"
    ],
    [
      "0.000000000",
      "0.642421508"
    ],
    [
      "0.163907172",
      "0.651579409"
    ],
    [
      "0.340425134",
      "0.659574865"
    ],
    [
      "0.253032307",
      "0.686947252"
    ],
    [
      "0.079309932",
      "0.688211118"
    ],
    [
      "0.000000000",
      "0.734000729"
    ],
    [
      "0.135079639",
      "0.760850594"
    ],
    [
      "0.225659921",
      "0.774340079"
    ],
    [
      "0.055769707",
      "0.806640205"
    ],
    [
      "0.148569124",
      "0.851430876"
    ],
    [
      "0.000000000",
      "0.879279681"
    ],
    [
      "0.083812835",
      "0.916187165"
    ],
    [
      "0.002289460",
      "0.994276278"
    ]
  ]
}

提交点集坐标 points,每个坐标均以十进制字符串表示(如 "0.5")。 · 验证器 v1.0.0

DISCUSSION

讨论区

欢迎交流解题思路、分享构造方法或探讨卡壳之处。所有登录用户均可发帖;发言使用与排行榜一致的公开署名,并附带唯一注册编号(#UID)以防冒名。新发言经审核后公开。