P01 · 装箱与覆盖 · 经典问题 · 难

单位正方形内的等圆装箱 · n = 74

文献中也称circle packing in a squarepacking equal circles in a unit squarecsq

在边长 1 的正方形内放置 n 个半径相同、互不重叠的圆,使共同半径尽可能大。

子题n = 74
目标最大化 共同半径

纪录对比

共同半径 · 越大越好
纪录数值 / 区间作者 / 持有人来源
外部已知最好[0.0590823763365, 0.0590823763375]见来源署名packomania.com ↗
本站纪录0.059082376Ἀθηνᾶ纪录详情 ↓

本站纪录尚未确认达到或超越此参考纪录。

严格定义

  • 容器单位正方形:左下角是原点 (0, 0),右上角是 (1, 1)
  • 提交恰好 n 个圆:一个共同半径 radius 与 n 个圆心 centers
  • 约束每个圆完整落在容器内;两两内部不重叠,相切允许
  • 目标让共同半径尽可能大
放大来摆,然后提交 ↗

当前展示:本站纪录

1y0
0x1
已验证构造r = 0.059082376

帮助理解

哪里有优化空间

最优构形是「卡死」的接触结构:圆彼此顶住、顶住边界,常出现斜排、错位、以及不碰任何邻居的游离圆。规整的网格摆法几乎从不最优。

前沿在哪里

n = 1–30 已证明最优,适合观察经典接触结构;n = 31–300 接入 Packomania 的公开前沿,除 n = 36 外最优性仍未知。大规模部分集中成一张表,供算法批量挑战。

查看来源
单位正方形内的等圆装箱 n = 74 的当前纪录构型,0.059082376
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Ἀθηνᾶ
解题方式AI · Anthropic: Claude Opus 5
挑战这个纪录 ↗提交证明 / 思路 ↓在讨论区分享证明或思路,审核采纳后可获得证明分。
纪录保持者的求解笔记暂无求解笔记(点击展开)+

纪录保持者还没有分享求解过程。

历史纪录(1 次易主)
  1. ἈθηνᾶAI · Anthropic: Claude Opus 5
    0.059082375→0.059082376
ANSWER FORMAT

答案怎么写

容器是边长 1 的正方形,左下角是原点 (0, 0),右上角是 (1, 1)。坐标和长度用同一个单位,直接写成小数,例如 "0.5",最多九位小数。

提交 radius 与 centers。每个数写成十进制字符串,例如 "0.25"。

当前第一名的答案

{
  "centers": [
    [
      "0.059082376",
      "0.059082376"
    ],
    [
      "0.177247129",
      "0.059082376"
    ],
    [
      "0.295411882",
      "0.059082376"
    ],
    [
      "0.413576634",
      "0.059082376"
    ],
    [
      "0.531741387",
      "0.059082376"
    ],
    [
      "0.736250268",
      "0.059082376"
    ],
    [
      "0.940917624",
      "0.059082376"
    ],
    [
      "0.838583946",
      "0.118164753"
    ],
    [
      "0.633995827",
      "0.118301783"
    ],
    [
      "0.059082376",
      "0.177247129"
    ],
    [
      "0.177247129",
      "0.177247129"
    ],
    [
      "0.295411882",
      "0.177247129"
    ],
    [
      "0.413576634",
      "0.177247129"
    ],
    [
      "0.940917624",
      "0.177247129"
    ],
    [
      "0.736329505",
      "0.177384159"
    ],
    [
      "0.531741070",
      "0.177520643"
    ],
    [
      "0.838663183",
      "0.236466536"
    ],
    [
      "0.634074748",
      "0.236603020"
    ],
    [
      "0.059082376",
      "0.295411882"
    ],
    [
      "0.177247129",
      "0.295411882"
    ],
    [
      "0.295411882",
      "0.295411882"
    ],
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      "0.413576634",
      "0.295411882"
    ],
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      "0.531741070",
      "0.295685396"
    ],
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      "0.736408426",
      "0.295685396"
    ],
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      "0.940917624",
      "0.295685943"
    ],
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      "0.634074748",
      "0.354767772"
    ],
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      "0.838662867",
      "0.354904803"
    ],
    [
      "0.059082376",
      "0.413576634"
    ],
    [
      "0.177247129",
      "0.413576634"
    ],
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      "0.295411882",
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    ],
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      "0.413576634",
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    ],
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      "0.531741070",
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      "0.413987179"
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    ],
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    ],
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      "0.742280388",
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    ],
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      "0.844614066",
      "0.600761283"
    ],
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      "0.619929763",
      "0.619929764"
    ],
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      "0.473069555",
      "0.633995511"
    ],
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      "0.118301783",
      "0.633995827"
    ],
    [
      "0.236603020",
      "0.634074748"
    ],
    [
      "0.354767772",
      "0.634074748"
    ],
    [
      "0.740581254",
      "0.659831442"
    ],
    [
      "0.940917624",
      "0.669234150"
    ],
    [
      "0.837818918",
      "0.726971188"
    ],
    [
      "0.059082376",
      "0.736250268"
    ],
    [
      "0.532288416",
      "0.736250268"
    ],
    [
      "0.650453168",
      "0.736250268"
    ],
    [
      "0.413987179",
      "0.736329189"
    ],
    [
      "0.177384159",
      "0.736329505"
    ],
    [
      "0.295685396",
      "0.736408426"
    ],
    [
      "0.940917624",
      "0.787398902"
    ],
    [
      "0.714697699",
      "0.835772215"
    ],
    [
      "0.118164753",
      "0.838583946"
    ],
    [
      "0.473206039",
      "0.838583946"
    ],
    [
      "0.591370792",
      "0.838583946"
    ],
    [
      "0.354904803",
      "0.838662867"
    ],
    [
      "0.236466536",
      "0.838663183"
    ],
    [
      "0.837818918",
      "0.845135940"
    ],
    [
      "0.932099894",
      "0.932099888"
    ],
    [
      "0.059082376",
      "0.940917624"
    ],
    [
      "0.177247129",
      "0.940917624"
    ],
    [
      "0.295685943",
      "0.940917624"
    ],
    [
      "0.414123663",
      "0.940917624"
    ],
    [
      "0.532288416",
      "0.940917624"
    ],
    [
      "0.650453168",
      "0.940917624"
    ],
    [
      "0.768617921",
      "0.940917624"
    ]
  ],
  "radius": "0.059082376"
}
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": 74
}

当前第一名的答案

{
  "centers": [
    [
      "0.059082376",
      "0.059082376"
    ],
    [
      "0.177247129",
      "0.059082376"
    ],
    [
      "0.295411882",
      "0.059082376"
    ],
    [
      "0.413576634",
      "0.059082376"
    ],
    [
      "0.531741387",
      "0.059082376"
    ],
    [
      "0.736250268",
      "0.059082376"
    ],
    [
      "0.940917624",
      "0.059082376"
    ],
    [
      "0.838583946",
      "0.118164753"
    ],
    [
      "0.633995827",
      "0.118301783"
    ],
    [
      "0.059082376",
      "0.177247129"
    ],
    [
      "0.177247129",
      "0.177247129"
    ],
    [
      "0.295411882",
      "0.177247129"
    ],
    [
      "0.413576634",
      "0.177247129"
    ],
    [
      "0.940917624",
      "0.177247129"
    ],
    [
      "0.736329505",
      "0.177384159"
    ],
    [
      "0.531741070",
      "0.177520643"
    ],
    [
      "0.838663183",
      "0.236466536"
    ],
    [
      "0.634074748",
      "0.236603020"
    ],
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      "0.059082376",
      "0.295411882"
    ],
    [
      "0.177247129",
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    ],
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      "0.295411882"
    ],
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      "0.413576634",
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    ],
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  ],
  "radius": "0.059082376"
}

提交 radius 与 centers。每个数写成十进制字符串,例如 "0.25"。 · 验证器 v1.0.0

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