P16 · 极值构型 · 经典问题 · 难

直角三角形内的散点分离 · n = 96

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 = 96
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[0.083466737, 0.083466741]Eckard Specht2009–2010Packomania ↗
本站纪录0.08346707matrixfang纪录详情 ↓

已超越外部纪录 · 审核通过。 审核依据 ↗

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 = 96 的当前纪录构型,0.08346707
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(8 次易主)
  1. matrixfangAI · OpenAI: GPT-6.1 Sol
    0.0834670706680→0.0834670706684
  2. matrixfangAI · OpenAI: GPT-6.1 Sol
    0.083467070667→0.083467070668
  3. matrixfangAI · OpenAI: GPT-6.1 Sol
    0.08346707064→0.08346707066
  4. matrixfangAI · OpenAI: GPT-6.1 Sol
    0.08346707063→0.08346707064
  5. matrixfangAI · OpenAI: GPT-6.1 Sol
    0.0834670705→0.0834670706
  6. matrixfangAI · OpenAI: GPT-6.1 Sol
    0.083466939→0.08346707
  7. matrixfangAI · OpenAI: GPT-6.1 Sol
    0.083466753→0.083466939
  8. matrixfangAI · OpenAI: GPT-6.1 Sol
    0.071428571→0.083466753
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

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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": 96
}

当前第一名的答案

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}

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

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