{"id":43425,"date":"2023-06-30T19:23:54","date_gmt":"2023-06-30T11:23:54","guid":{"rendered":"https:\/\/swarma.org\/?p=43425"},"modified":"2023-06-30T19:23:54","modified_gmt":"2023-06-30T11:23:54","slug":"%e4%ba%ba%e5%b7%a5%e6%99%ba%e8%83%bd%e5%89%8d%e6%b2%bf%ef%bc%9a%e7%bb%84%e5%90%88%e4%bc%98%e5%8c%96%e9%97%ae%e9%a2%98%e7%9a%84%e6%9c%ba%e5%99%a8%e5%ad%a6%e4%b9%a0%e6%b1%82%e8%a7%a3-%e8%8c%83","status":"publish","type":"post","link":"https:\/\/swarma.org\/?p=43425","title":{"rendered":"\u4eba\u5de5\u667a\u80fd\u524d\u6cbf\uff1a\u7ec4\u5408\u4f18\u5316\u95ee\u9898\u7684\u673a\u5668\u5b66\u4e60\u6c42\u89e3 | \u8303\u957f\u4fca\u5206\u4eab\u6574\u7406"},"content":{"rendered":"
\n
\n
\n
\n

<\/p>\n<\/section>\n

\n
\n
\n
\n
\n
\n

<\/section>\n<\/section>\n
\n
\n

\u5bfc\u8bed<\/strong><\/p>\n<\/section>\n<\/section>\n

\n

<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n
\n
\n
\n

\u73b0\u5b9e\u4e16\u754c\u4e2d\u5927\u91cf\u95ee\u9898\u7684\u89e3\u51b3\u9700\u8981\u8bbe\u8ba1\u548c\u6c42\u89e3\u7b97\u6cd5\u3002\u4f20\u7edf\u4e0a\u901a\u5e38\u662f\u4eba\u7c7b\u5b66\u4e60\u5982\u4f55\u8bbe\u8ba1\u7b97\u6cd5\uff0c\u800c\u968f\u7740\u4eba\u5de5\u667a\u80fd\u6280\u672f\u7684\u53d1\u5c55\uff0c\u6211\u4eec\u4e5f\u53ef\u4ee5\u901a\u8fc7\u673a\u5668\u5b66\u4e60\u65b9\u6cd5\u53bb\u5b66\u4e60\u5982\u4f55\u8bbe\u8ba1\u7b97\u6cd5\u3002\u5bf9\u4e8e\u7ec4\u5408\u4f18\u5316\u95ee\u9898\uff0c\u673a\u5668\u5b66\u4e60\u7b97\u6cd5\u80fd\u591f\u6c42\u89e3\u8d85\u5927\u89c4\u6a21\u7684\u590d\u6742\u7cfb\u7edf\u95ee\u9898\uff0c\u5177\u6709\u5f3a\u5927\u7684\u4f18\u52bf\u3002<\/span><\/strong><\/p>\n


<\/span><\/strong><\/p>\n

\u5728\u300c\u56fe\u795e\u7ecf\u7f51\u7edc\u4e0e\u7ec4\u5408\u4f18\u5316<\/a>\u300d\u8bfb\u4e66\u4f1a\u7b2c\u4e00\u671f\u5206\u4eab\u4e2d\uff0c\u56fd\u9632\u79d1\u6280\u5927\u5b66\u7cfb\u7edf\u5de5\u7a0b\u5b66\u9662\u526f\u6559\u6388\u8303\u957f\u4fca\u4ecb\u7ecd\u4e86\u673a\u5668\u5b66\u4e60\u6c42\u89e3\u7ec4\u5408\u4f18\u5316\u7684\u95ee\u9898\uff0c\u7279\u522b\u662f\u56fe\u4e0a\u7684\u7ec4\u5408\u4f18\u5316\u95ee\u9898\uff0c\u5305\u62ec\u6700\u5c0f\u8282\u70b9\u8986\u76d6\u3001\u7f51\u7edc\u74e6\u89e3\u3001\u81ea\u65cb\u73bb\u7483\u57fa\u6001\u6c42\u89e3\u95ee\u9898\u7684\u6700\u65b0\u8fdb\u5c55\uff0c\u5e76\u8fdb\u4e00\u6b65\u63a2\u8ba8\u4e86\u4e00\u4e9b\u6709\u610f\u601d\u7684\u7814\u7a76\u8fdb\u5c55\u3002\u672c\u6587\u662f\u6b64\u6b21\u8bfb\u4e66\u4f1a\u5206\u4eab\u7684\u6574\u7406\u3002<\/span><\/strong><\/p>\n


<\/span><\/strong><\/p>\n

\u6b22\u8fce\u626b\u7801\u89c2\u770b\u8bfb\u4e66\u4f1a\u89c6\u9891\u56de\u653e\uff1a<\/span><\/strong><\/p>\n

<\/p>\n

<\/strong><\/p>\n

https:\/\/pattern.swarma.org\/study_group_issue\/483?from=wechat<\/span><\/span><\/p>\n<\/section>\n

\n


<\/p>\n<\/section>\n

\u5173\u952e\u8bcd\uff1a\u56fe\u795e\u7ecf\u7f51\u7edc\uff0c\u7ec4\u5408\u4f18\u5316\uff0c\u4eba\u5de5\u667a\u80fd\uff0c\u673a\u5668\u5b66\u4e60<\/strong><\/strong><\/span><\/strong><\/span><\/strong><\/span><\/strong><\/span><\/strong><\/span><\/strong><\/span><\/strong><\/strong><\/strong><\/span><\/strong><\/span><\/strong><\/strong><\/strong><\/strong><\/strong><\/span><\/strong><\/span><\/strong><\/span><\/strong><\/span><\/strong><\/span><\/strong><\/span><\/strong><\/strong><\/strong><\/span><\/strong><\/span><\/strong><\/strong><\/strong><\/strong><\/strong><\/span><\/strong><\/span><\/strong><\/span><\/strong><\/span><\/strong><\/span><\/strong><\/span><\/strong><\/strong><\/strong><\/span><\/strong><\/span><\/strong><\/strong><\/strong><\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n
\n
\n
\n
<\/svg><\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n
\n
<\/strong><\/section>\n<\/section>\n
\n

\u8303\u957f\u4fca<\/span> <\/span>| \u8bb2\u8005<\/strong><\/span><\/span><\/p>\n

\u738b\u5fd7\u9e4f<\/span> <\/span>| \u6574\u7406<\/span><\/strong><\/span><\/p>\n

\u6881\u91d1<\/span> <\/span>| \u7f16\u8f91<\/strong><\/span><\/strong><\/p>\n


<\/strong><\/span><\/strong><\/p>\n


<\/p>\n<\/section>\n

\n
\n
\n
\n
\n

<\/section>\n

<\/section>\n
\n
<\/svg><\/section>\n<\/section>\n<\/section>\n
\n

\u4e00\u3001\u7ec4\u5408\u4f18\u5316\u95ee\u9898\u4e0e\u673a\u5668\u5b66\u4e60<\/strong><\/strong><\/p>\n<\/section>\n

\n

<\/section>\n

<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/h3>\n
  <\/span><\/section>\n
\u7ec4\u5408\u4f18\u5316\u662f\u901a\u8fc7\u5bf9\u6570\u5b66\u65b9\u6cd5\u7684\u7814\u7a76\u5bfb\u627e\u79bb\u6563\u4e8b\u4ef6\u7684\u6700\u4f18\u7f16\u6392\u3001\u5206\u7ec4\u3001\u6b21\u5e8f\u6216\u7b5b\u9009\uff0c\u662f\u8fd0\u7b79\u5b66\u7684\u4e00\u4e2a\u91cd\u8981\u72ec\u7acb\u5206\u652f\uff0c\u662f\u4e00\u7c7b\u91cd\u8981\u7684\u4f18\u5316\u95ee\u9898\u3002\u7ec4\u5408\u4f18\u5316\u5728\u73b0\u5b9e\u4e2d\u7684\u5e94\u7528\u4e5f\u5341\u5206\u5e7f\u6cdb\uff0c\u5305\u62ec\u5728\u62cd\u5356\u8bbe\u8ba1\u3001\u8d44\u6e90\u8c03\u5ea6\u3001\u751f\u6210\u3001\u7269\u6d41\u4ee5\u53ca\u519b\u4e8b\u9886\u57df\u7b49\u90fd\u6709\u5e7f\u6cdb\u5e94\u7528\u3002\u6b64\u5916\uff0c\u5e38\u89c1\u7684\u9700\u8981\u7ec4\u5408\u4f18\u5316\u6765\u6c42\u89e3\u7684\u7ecf\u5178\u95ee\u9898\u5305\u62ec\uff1a0-1\u80cc\u5305\u95ee\u9898\u3001\u65c5\u884c\u5546\u95ee\u9898<\/span>\uff08TSP\uff09<\/span>\u3001\u6700\u5c0f\u8282\u70b9\u8986\u76d6\u95ee\u9898<\/span>\uff08MVC\uff09<\/span>\u3001\u6700\u5c0f\u652f\u914d\u96c6\u95ee\u9898<\/span>\uff08MDS\uff09<\/span>\u4ee5\u53ca\u6700\u5927\u5272\u95ee\u9898<\/span>\uff08Max-Cut\uff09<\/span>\u7b49\u3002<\/span><\/section>\n

<\/section>\n

\u76ee\u524d\u7ec4\u5408\u4f18\u5316\u7b97\u6cd5\u4e3b\u8981\u53ef\u4ee5\u5206\u4e3a\u4ee5\u4e0b\u56db\u7c7b\uff1a<\/span><\/p>\n

1\uff09\u6570\u5b66\u4f18\u5316\u7b97\u6cd5\uff0c\u5305\u62ec\u5206\u652f\u5b9a\u754c\u3001\u5206\u652f\u5b9a\u4ef7\u3001\u5206\u652f\u5207\u5272\u7b49\u65b9\u6cd5\uff0c\u8fd9\u662f\u4e00\u79cd\u7cbe\u786e\u7b97\u6cd5\uff0c\u8be5\u65b9\u6cd5\u4f18\u70b9\u662f\u80fd\u627e\u5230\u5168\u5c40\u6700\u4f18\u89e3\uff0c\u5e76\u4e14\u80fd\u6709\u7406\u8bba\u4fdd\u8bc1\uff0c\u7f3a\u70b9\u5728\u4e8e\u65f6\u95f4\u5448\u6307\u6570\u589e\u957f\uff0c\u4e0d\u9002\u5408\u6c42\u89e3\u5927\u89c4\u6a21\u7684\u95ee\u9898\uff1b<\/span><\/p>\n

2\uff09\u542f\u53d1\u5f0f\u7b97\u6cd5\uff0c\u5305\u62ec\u8fd1\u4f3c\u7b97\u6cd5\u3001\u8d2a\u5a6a\u7b97\u6cd5\u7b49\uff0c\u8fd9\u7c7b\u7b97\u6cd5\u4f18\u70b9\u662f\u5b58\u5728\u6700\u5dee\u89e3\u7684\u8d28\u91cf\u4fdd\u8bc1\uff0c\u7f3a\u70b9\u662f\u6c42\u89e3\u65f6\u95f4\u6bd4\u8f83\u957f\uff0c\u5e76\u4e14\u8fd9\u7c7b\u8fd1\u4f3c\u7b97\u6cd5\u5f88\u96be\u6784\u9020\uff1b<\/span><\/p>\n

3\uff09\u5143\u542f\u53d1\u5f0f\u7b97\u6cd5\uff0c\u5982\u9057\u4f20\u7b97\u6cd5\u3001\u8681\u7fa4\u7b97\u6cd5\u3001\u6a21\u62df\u9000\u706b\u7b97\u6cd5\u7b49\uff0c\u8fd9\u7c7b\u7b97\u6cd5\u4f18\u70b9\u662f\u6bd4\u8f83\u5bb9\u6613\u5b9e\u73b0\uff0c\u6c42\u89e3\u65f6\u95f4\u4e5f\u4e0d\u4f1a\u592a\u957f\uff0c\u5f88\u5feb\u5f97\u5230\u4e00\u4e2a\u53ef\u884c\u89e3\uff0c\u7f3a\u70b9\u5728\u4e8e\u8be5\u7c7b\u7b97\u6cd5\u5bf9\u53c2\u6570\u6bd4\u8f83\u654f\u611f\uff0c\u4e0d\u540c\u7684\u5b9e\u4f8b\u90fd\u9700\u8981\u91cd\u65b0\u8c03\u6574\u53c2\u6570\uff1b<\/span><\/p>\n

4) \u673a\u5668\u5b66\u4e60\u65b9\u6cd5\uff0c\u5305\u62ec\u76d1\u7763\u5b66\u4e60\u3001\u5f3a\u5316\u5b66\u4e60\u4ee5\u53ca\u56fe\u795e\u7ecf\u7f51\u7edc\u7b49\uff0c\u8be5\u7c7b\u65b9\u6cd5\u4f18\u70b9\u662f\u80fd\u4ece\u540c\u5206\u5e03\u7684\u6570\u636e\u4e2d\u5b66\u4e60\u5230\u901a\u7528\u7684\u89c4\u5f8b\uff0c\u80fd\u5feb\u901f\u6c42\u89e3\u540c\u4e00\u7c7b\u95ee\u9898\u3002<\/span><\/section>\n

<\/section>\n
\u603b\u4e4b\uff0c\u524d\u4e09\u7c7b\u7b97\u6cd5\u53ef\u4ee5\u770b\u6210\u662f\u4eba\u7c7b\u5b66\u4e60\u5982\u4f55\u53bb\u8bbe\u8ba1\u7b97\u6cd5\uff0c\u8be5\u7c7b\u7b97\u6cd5\u89c4\u6a21\u9002\u4e2d\u3001\u6a21\u578b\u76f8\u5bf9\u7a33\u5b9a\u3001\u80fd\u6c42\u89e3\u4e00\u4e9b\u7ecf\u5178\u95ee\u9898\u540c\u65f6\u4e5f\u5b58\u5728\u7406\u8bba\u4fdd\u8bc1\uff0c\u800c\u6700\u540e\u4e00\u7c7b\u65b9\u6cd5\u662f\u901a\u8fc7\u673a\u5668\u5b66\u4e60\u65b9\u6cd5\u53bb\u5b66\u4e60\u5982\u4f55\u8bbe\u8ba1\u7ec4\u5408\u4f18\u5316\u7b97\u6cd5\uff0c\u8be5\u7c7b\u7b97\u6cd5\u80fd\u6c42\u89e3\u8d85\u5927\u89c4\u6a21\u95ee\u9898\uff0c\u662f\u5b9e\u65f6\u53d8\u5316\u7684\uff0c\u80fd\u5904\u7406\u590d\u6742\u7cfb\u7edf\uff0c\u552f\u4e00\u7684\u7f3a\u70b9\u662f\u8be5\u7c7b\u7b97\u6cd5\u662f\u4e00\u79cd\u5f31\u6700\u4f18\u6027<\/strong>\u3002\u9664\u4e86\u4e0a\u8ff0\u56db\u7c7b\u4f18\u5316\u7b97\u6cd5\u4ee5\u5916\uff0c\u4e5f\u5b58\u5728\u4e00\u4e9b\u7ec4\u5408\u4f18\u5316\u7684\u6c42\u89e3\u5668\uff0c\u5305\u62ec\u5546\u7528\u6c42\u89e3\u5668\uff0cGurobi\u3001CPLEX\u7b49\u4ee5\u53ca\u5f00\u6e90\u6c42\u89e3\u5668\uff0cSCIP\u4ee5\u53caLP_Solve\u7b49\u3002<\/span><\/section>\n

<\/span><\/section>\n
\u76ee\u524d\u673a\u5668\u5b66\u4e60\u7ec4\u5408\u4f18\u5316\u662f\u4eba\u5de5\u667a\u80fd\u7684\u524d\u6cbf\u65b9\u5411\uff0c\u53ef\u4ee5\u901a\u8fc7\u4f7f\u7528\u4e0d\u540c\u7684\u673a\u5668\u5b66\u4e60\u65b9\u6cd5\u6765\u89e3\u51b3\u4e0d\u540c\u7684\u7ec4\u5408\u4f18\u5316\u95ee\u9898\uff0c\u5982\u56fe\u4f18\u5316\u4ee5\u53ca\u79bb\u6563\u4f18\u5316\u7b49\u3002<\/span><\/section>\n

<\/section>\n
<\/section>\n
\u56fe1 \u7ec4\u5408\u4f18\u5316\u4e0e\u673a\u5668\u5b66\u4e60<\/span><\/section>\n

<\/section>\n

\u76ee\u524d\u7ec4\u5408\u4f18\u5316\u95ee\u9898\u7684\u673a\u5668\u5b66\u4e60\u6c42\u89e3\u8303\u5f0f\u53ef\u4ee5\u5206\u4e3a\u4e24\u7c7b\uff1a<\/span><\/p>\n

1\uff09\u57fa\u4e8e\u7aef\u5230\u7aef\u6c42\u89e3<\/strong>\uff0c\u8f93\u5165\u662f\u95ee\u9898\u7684\u5b9e\u4f8b\u8f93\u51fa\u662f\u95ee\u9898\u7684\u7ed3\u679c\uff0c\u4f7f\u7528\u81ea\u56de\u5f52\u6216\u8005\u975e\u81ea\u56de\u5f52\u7684\u65b9\u6cd5\u5b66\u4e60\u4e00\u4e9b\u542f\u53d1\u5f0f\u7684\u7b56\u7565\uff1b<\/span><\/p>\n

2\uff09\u5c40\u90e8\u6539\u8fdb\u6c42\u89e3<\/strong>\uff0c\u5305\u62ec\u6539\u8fdb\u7cbe\u786e\u7b97\u6cd5\u6216\u8005\u542f\u53d1\u5f0f\u7b97\u6cd5\u7b49\uff0c\u5c06\u8fd9\u4e9b\u7b97\u6cd5\u4e2d\u6bd4\u8f83\u8017\u65f6\u7684\u6a21\u5757\u8f6c\u6362\u6210\u4e00\u4e2a\u5b66\u4e60\u95ee\u9898\uff0c\u4ece\u800c\u52a0\u5feb\u6c42\u89e3\u7684\u901f\u5ea6\uff0c\u5982\u5bf9\u4e8e\u5206\u652f\u5b9a\u754c\u7b97\u6cd5\uff0c \u53ef\u4ee5\u5c06\u4f20\u7edf\u7684\u4f7f\u7528\u5f3a\u8fde\u901a\u91cf\u8fdb\u884c\u5206\u652f\u8f6c\u6362\u6210\u4e00\u4e2a\u4e8c\u5206\u7c7b\u7684\u5b66\u4e60\u95ee\u9898\u3002<\/span><\/section>\n

<\/span><\/section>\n
\n
\n
\u4ec0\u4e48\u662f\u7ec4\u5408\u4f18\u5316 | \u96c6\u667a\u767e\u79d1<\/a><\/section>\n<\/section>\n<\/blockquote>\n

<\/section>\n
\n


<\/p>\n<\/section>\n

\n
\n
\n
\n
\n

<\/section>\n

<\/section>\n
\n
<\/svg><\/section>\n<\/section>\n<\/section>\n
\n

\u4e8c\u3001\u56fe\u4e0a\u7684\u7ec4\u5408\u4f18\u5316\u95ee\u9898\u6c42\u89e3<\/strong><\/strong><\/p>\n<\/section>\n

\n

<\/section>\n

<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/h3>\n
  <\/span><\/section>\n
\u56fe\u4e0a\u7684\u7ec4\u5408\u4f18\u5316\u95ee\u9898\u662f\u7ec4\u5408\u4f18\u5316\u95ee\u9898\u4e2d\u5f88\u91cd\u8981\u7684\u90e8\u5206\uff0c\u5f88\u591a\u56fe\u4e0a\u7684\u7ec4\u5408\u4f18\u5316\u95ee\u9898\u90fd\u662fNPC\u95ee\u9898\uff0c\u53ea\u8981\u8bc1\u660e\u4efb\u610f\u4e00\u4e2aNPC\u95ee\u9898\u5b58\u5728\u7cbe\u786e\u7684\u591a\u9879\u5f0f\u7b97\u6cd5\uff0c\u90a3\u4e48\u6240\u6709\u7684NP\u95ee\u9898\u5c31\u90fd\u89e3\u51b3\u4e86\uff0c\u90a3\u4e48P=NP\u3002<\/span><\/section>\n

<\/span><\/section>\n
\n
\n
\u4ec0\u4e48\u662fP\u95ee\u9898\u3001NP\u95ee\u9898\u548cNPC\u95ee\u9898\uff1f<\/a><\/section>\n<\/section>\n<\/blockquote>\n

<\/section>\n
\u56fe\u4e0a\u7684\u7ec4\u5408\u4f18\u5316\u95ee\u9898\u7684\u4e00\u822c\u6027\u6c42\u89e3\u6846\u67b6\u5177\u4f53\u5206\u4e3a\u4e24\u4e2a\u9636\u6bb5\uff1a\u7b2c\u4e00\u4e2a\u9636\u6bb5\u8fdb\u884c\u56fe\u7684\u8868\u793a\u5b66\u4e60\uff0c\u5b66\u4e60\u8282\u70b9\u6216\u8fb9\u7684\u8868\u793a\u5411\u91cf\uff1b\u7b2c\u4e8c\u4e2a\u9636\u6bb5\u57fa\u4e8e\u8868\u793a\u5411\u91cf\u6c42\u89e3\u7ec4\u5408\u4f18\u5316\u95ee\u9898\uff0c\u53ef\u4ee5\u4f7f\u7528\u975e\u81ea\u56de\u5f52\u65b9\u6cd5\u7ed3\u5408\u542f\u53d1\u5f0f\u641c\u7d22\u89c4\u5219\uff0c\u6216\u8005\u57fa\u4e8e\u5f3a\u5316\u5b66\u4e60\u7684\u81ea\u56de\u5f52\u8fdb\u884c\u4f18\u5316\u6c42\u89e3\u3002\u4e0b\u9762\u5206\u522b\u5177\u4f53\u4ecb\u7ecd\u8fd9\u4e24\u7c7b\u65b9\u6cd5\u3002<\/span><\/section>\n

<\/span><\/section>\n
<\/section>\n
\u56fe2 \u7ec4\u5408\u4f18\u5316\u95ee\u9898\u7684\u4e00\u822c\u6027\u6c42\u89e3\u6846\u67b6<\/span><\/section>\n

<\/section>\n
1. \u975e\u81ea\u56de\u5f52\u65b9\u6cd5<\/span><\/strong><\/section>\n

<\/section>\n
\n
\n
\n
Combinatorial Optimization with Graph Convolutional Networks and Guided Tree Search<\/span><\/section>\n
https:\/\/arxiv.org\/abs\/1810.10659<\/span><\/section>\n<\/section>\n<\/section>\n<\/blockquote>\n

<\/section>\n
\u8be5\u8bba\u6587\u5c1d\u8bd5\u89e3\u51b3\u6700\u5c0f\u652f\u914d\u96c6\u3001\u6700\u5c0f\u8282\u70b9\u8986\u76d6\u4ee5\u53ca\u6700\u5927\u5272\u8fd9\u6837\u7684NPC\u95ee\u9898\uff0c\u4f7f\u7528\u76d1\u7763\u5b66\u4e60\u7b97\u6cd5\u7ed3\u5408\u6811\u641c\u7d22\u4ee5\u53ca\u56fe\u8bba\u6280\u5de7\u6765\u5b66\u4e60\u3002\u5177\u4f53\u6765\u8bf4\uff0c\u6a21\u578b\u8f93\u5165\u4e00\u4e2a\u56fe\uff0c\u7136\u540e\u5bf9\u56fe\u8fdb\u884c\u7ea6\u7b80\uff0c\u5c06\u7ea6\u7b80\u540e\u7684\u7b49\u4ef7\u56fe\u8f93\u5165GCN\u4e2d\u5b66\u4e60\u6bcf\u4e2a\u8282\u70b9\u9009\u4e2d\u4e0e\u5426\u7684\u6982\u7387\uff0c\u7136\u540e\u7ed3\u5408\u6811\u641c\u7d22\u7b97\u6cd5\u4ee5\u53ca\u56fe\u8bba\u7684\u5c40\u90e8\u641c\u7d22\u7b97\u6cd5\u9009\u62e9\u6700\u4f18\u7684\u7ed3\u679c\uff0c\u8f93\u51fa\u56fe\u4e2d\u7684\u6bcf\u4e2a\u8282\u70b9\u662f\u5426\u88ab\u9009\u4e2d\u3002\u635f\u5931\u51fd\u6570\u662f\u4e00\u4e2a\u4ea4\u53c9\u71b5\u635f\u5931\uff0c\u8868\u793a\u7b2ci\u4e2a\u8bad\u7ec3\u56fe\u7684\u7b2cj\u4e2a\u8282\u70b9\u7684\u6807\u7b7e\uff0c\u8868\u793aGCN\u8f93\u51fa\u7684\u7b2cj\u4e2a\u8282\u70b9\u9009\u4e2d\u4e0e\u5426\u7684\u6982\u7387\u3002\u4e3a\u4e86\u89e3\u51b3\u7ec4\u5408\u4f18\u5316\u89e3\u4e0d\u552f\u4e00\u7684\u60c5\u51b5\uff0c\u4f5c\u8005\u4f7f\u7528 hindsight loss \u4f18\u5316GCN\u8f93\u51fa\u591a\u4e2aloss\u7684\u6700\u5c0f\u503c\u3002\u5176\u4e2d\u901a\u8fc7\u540e\u9762\u7684\u5b9e\u9a8c\u53d1\u73b0\u56fe\u8bba\u6280\u5de7\u5bf9\u4e8e\u6027\u80fd\u7684\u63d0\u5347\u5360\u4e86\u5f88\u5927\u6bd4\u91cd\uff0c\u53bb\u6389\u5c40\u90e8\u641c\u7d22\u7684\u6280\u5de7\uff0c\u9884\u6d4b\u7684\u6027\u80fd\u663e\u8457\u964d\u4f4e\u3002\u4f46\u662f\u8be5\u7c7b\u7b97\u6cd5\u7684\u7f3a\u70b9\u5728\u4e8e\u6cdb\u5316\u6548\u679c\u6bd4\u8f83\u5dee\uff0c \u9664\u975e\u52a0\u5f88\u591a\u9886\u57df\u7684\u4e13\u4e1a\u6280\u5de7\u624d\u80fd\u63d0\u9ad8\u6027\u80fd\u3002<\/span><\/section>\n

<\/span><\/section>\n
<\/section>\n
<\/section>\n

<\/section>\n
<\/section>\n
\u56fe3 \u57fa\u4e8e\u56fe\u5377\u79ef\u7f51\u7edc\u548c\u5f15\u5bfc\u6811\u641c\u7d22\u7684\u7ec4\u5408\u4f18\u5316\u7b97\u6cd5\u6d41\u7a0b\u56fe<\/span><\/section>\n

<\/section>\n
2. \u81ea\u56de\u5f52\u65b9\u6cd5<\/span><\/strong><\/section>\n

<\/section>\n
\u7b2c\u4e8c\u7c7b\u4e3a\u81ea\u56de\u5f52\u7b97\u6cd5\uff0c\u5176\u6846\u67b6\u4e3b\u8981\u5c31\u662f\u7ed3\u5408\u56fe\u8868\u793a\u5b66\u4e60\u548c\u5f3a\u5316\u5b66\u4e60\u6765\u4f18\u5316\u76ee\u6807\u3002\u4e0b\u9762\u4e3b\u8981\u4ecb\u7ecd\u4e09\u4e2a\u5de5\u4f5c\uff0cS2V-DQN <\/span>(NIPS 2017)<\/span>\u3001FINDER <\/span>(NMI 2020)<\/span> \u4ee5\u53ca DIRAC<\/span> (NC 2023)<\/span>\u3002<\/span><\/section>\n

<\/span><\/section>\n

<\/span><\/section>\n
S2V-DQN<\/strong><\/span><\/section>\n
\n
\n
Khalil E, Dai H, Zhang Y, et al. Learning combinatorial optimization algorithms over graphs[J]. Advances in neural information processing systems, 2017, 30.<\/span><\/section>\n<\/section>\n<\/blockquote>\n

<\/section>\n
\u8be5\u5de5\u4f5c\u662f\u673a\u5668\u5b66\u4e60\u7ec4\u5408\u4f18\u5316\u9886\u57df\u7684\u91cc\u7a0b\u7891\u5f0f\u5de5\u4f5c\uff0c\u5f15\u9886\u5e76\u542f\u53d1\u4e86\u8be5\u9886\u57df\u540e\u7eed\u8bb8\u591a\u7684\u7814\u7a76\u3002\u8be5\u65b9\u6cd5\u7684\u6574\u4f53\u6846\u67b6\u662f\u8f93\u5165\u4e00\u4e2a\u56fe\uff0c\u7136\u540e\u4f7f\u7528\u591a\u5c42\u7684 Graph Embedding \u65b9\u6cd5\u5f97\u5230\u6bcf\u4e2a\u8282\u70b9\u7684\u8868\u793a\u5411\u91cf\uff0c\u7136\u540e\u901a\u8fc7Q-Learning\u7b97\u6cd5\u4e3a\u6bcf\u4e2a\u8282\u70b9\u7b97\u4e00\u4e2aQ-value\u4f5c\u4e3a\u8282\u70b9\u9009\u62e9\u7684\u957f\u671f\u6536\u76ca\uff0c\u6536\u76ca\u6700\u5927\u7684\u8282\u70b9\u88ab\u9009\u4e2d\uff0c\u7136\u540e\u91cd\u590dGraph Embedding\u548cQ-Learning\u76f4\u5230\u6ee1\u8db3\u7ec8\u6b62\u6761\u4ef6\u3002\u5176\u4e2d\u8fd9\u91cc\u7684Graph Embedding\u4f7f\u7528Struct2Vector\u7b97\u6cd5\u3002<\/span><\/section>\n

<\/span><\/section>\n
\u7b97\u6cd5\u8bad\u7ec3\u8fc7\u7a0b\u4e3b\u8981\u5305\u62ec\u56db\u4e2a\u90e8\u5206\uff1a1\uff09\u751f\u6210\u8bad\u7ec3\u56fe\u6570\u636e\uff1b2\uff09\u63a2\u7d22\u548c\u5229\u7528\u9009\u62e9\u8282\u70b9\uff1b3\uff09\u66f4\u65b0\u8282\u70b9\u7684\u72b6\u6001\uff1b4\uff09\u6784\u5efa\u635f\u5931\u51fd\u6570\u4f18\u5316\u6a21\u578b\u53c2\u6570\u3002\u8be5\u65b9\u6cd5\u5728\u6700\u5c0f\u8282\u70b9\u8986\u76d6\u3001\u6700\u5927\u5272\u4ee5\u53ca\u65c5\u884c\u5546\u95ee\u9898\u4e2d\u8fdb\u884c\u4e86\u5b9e\u9a8c\u3002\u5728\u6700\u5c0f\u8282\u70b9\u8986\u76d6\u5b9e\u9a8c\u4e2d\uff0c\u76f8\u6bd4CPLEX\u6c42\u89e3\u5668\u5f97\u5230\u7684\u6700\u4f18\u89e3\uff0c\u8be5\u65b9\u6cd5\u7684\u6027\u80fd\u51e0\u4e4e\u63a5\u8fd1\u6700\u4f18\u89e3\uff0c\u540c\u65f6\u8be5\u65b9\u6cd5\u4e5f\u5177\u6709\u5f88\u597d\u7684\u6cdb\u5316\u6027\uff0c\u5728\u5c0f\u89c4\u6a21\u56fe\u4e2d\u8fdb\u884c\u8bad\u7ec3\uff0c\u7136\u540e\u53ef\u4ee5\u7528\u6765\u5728\u5927\u56fe\u4e2d\u8fdb\u884c\u6d4b\u8bd5\u3002\u5728\u6700\u5927\u5272\u95ee\u9898\u4e2d\uff0c\u76f8\u6bd4\u4e8e\u57fa\u51c6\u7b97\u6cd5\u6765\u8bf4\u4e5f\u662f\u66f4\u4f18\u7684\u3002\u5728TSP\u95ee\u9898\u4e2d, \u5bf9\u4e8e\u5927\u56fe\u7684\u4f18\u5316\u6027\u80fd\u7a0d\u5fae\u900a\u8272\u4e9b\u3002<\/span><\/section>\n

<\/span><\/section>\n
<\/section>\n
\u56fe4 S2V-DQN\u7b97\u6cd5\u6d41\u7a0b\u56fe<\/span><\/section>\n

<\/section>\n
\u901a\u8fc7\u53ef\u89c6\u5316\u7684\u65b9\u5f0f\u5bf9\u7b97\u6cd5\u8fdb\u884c\u53ef\u89e3\u91ca\u5206\u6790\uff0c\u53d1\u73b0\u76f8\u6bd4\u5176\u4ed6\u7b97\u6cd5\uff0c\u5982\u6bcf\u6b65\u9009\u62e9\u8282\u70b9\u5ea6\u6700\u5927\u7684\u8282\u70b9\uff0c\u8be5\u7b97\u6cd5\u6bcf\u6b21\u90fd\u503e\u5411\u4e8e\u9009\u62e9\u90a3\u4e9b\u80fd\u8986\u76d6\u66f4\u591a\u8282\u70b9\uff0c\u540c\u65f6\u907f\u514d\u7834\u574f\u8fde\u901a\u6027\u7684\u8282\u70b9\uff0c\u4ece\u957f\u8fdc\u770b\uff0c\u9700\u8981\u7684\u8282\u70b9\u66f4\u5c11\uff0c\u8fd9\u4e5f\u6b63\u662f\u5f3a\u5316\u5b66\u4e60\u7684\u4f18\u52bf\uff0c\u53ef\u4ee5\u542f\u53d1\u6211\u4eec\u9009\u62e9\u66f4\u52a0\u9ad8\u6548\u7684\u8d2a\u5a6a\u7b97\u6cd5\u3002\u8be5\u7b97\u6cd5\u63d0\u4f9b\u4e86\u4e00\u4e2a\u7edf\u4e00\u6846\u67b6\uff0c\u9002\u7528\u591a\u79cd\u56fe\u4e0a\u7684\u7ec4\u5408\u4f18\u5316\u95ee\u9898<\/strong>\u3002<\/span><\/section>\n

<\/section>\n

<\/section>\n
FINDER<\/strong><\/span><\/section>\n
\n
\n
\n
Fan C, Zeng L, Sun Y, et al. Finding key players in complex networks through deep reinforcement learning[J]. Nature machine intelligence, 2020, 2(6): 317-324.<\/span><\/section>\n

<\/section>\n
\u8bba\u6587\u89e3\u8bfb\uff1a<\/span>\u7528\u5f3a\u5316\u5b66\u4e60\u5bfb\u627e\u5173\u952e\u8282\u70b9\u2014\u2014\u590d\u6742\u7f51\u7edc\u7814\u7a76\u65b0\u8303\u5f0f<\/a><\/span><\/section>\n<\/section>\n<\/section>\n<\/blockquote>\n

<\/section>\n
\u867d\u7136S2V-DQN\u65b9\u6cd5\u5df2\u7ecf\u5177\u6709\u5f88\u597d\u7684\u4f18\u5316\u6548\u679c\uff0c\u4f46\u662f\u5728\u66f4\u5927\u89c4\u6a21\u4e0a\u7684\u6cdb\u5316\u80fd\u529b\u8fd8\u6709\u5f85\u63d0\u9ad8\u3002\u56e0\u6b64\uff0c\u8303\u957f\u4fca\u7b49\u4eba\u63d0\u51fa\u4e00\u79cd\u901a\u7528\u4e14\u53ef\u6269\u5c55\u7684\u6df1\u5ea6\u5b66\u4e60\u6c42\u89e3\u6846\u67b6\uff0c\u9002\u7528\u4e8e\u4e0d\u540c\u7684\u74e6\u89e3\u573a\u666f\uff0c\u540c\u65f6\u7ee7\u7eed\u63d0\u5347\u4e86\u6a21\u578b\u7684\u6cdb\u5316\u80fd\u529b\uff0c\u4f7f\u5f97\u6a21\u578b\u80fd\u5728\u4e0a\u5343\u4e07\u8282\u70b9\u89c4\u6a21\u7684\u7f51\u7edc\u4e2d\u90fd\u80fd\u6709\u5f88\u597d\u7684\u6027\u80fd\u3002\u8be5\u5de5\u4f5c\u5c1d\u8bd5\u5728\u7f51\u7edc\u74e6\u89e3\u95ee\u9898\u4e0a\u9762\u5b9e\u9a8c\uff0c\u5e0c\u671b\u627e\u5230\u80fd\u4f7f\u7f51\u7edc\u74e6\u89e3\u7684\u6700\u5c11\u8282\u70b9\u96c6\u5408\u3002\u5176\u4e2d\u8861\u91cf\u6a21\u578b\u7684\u597d\u574f\u53ef\u4ee5\u4f7f\u7528ANC\u66f2\u7ebf\u4e0b\u7684\u9762\u79ef\u6765\u8868\u793a\uff0c\u7eb5\u8f74\u8868\u793a\u590d\u6742\u7f51\u8def\u529f\u80fd\uff0c\u6a2a\u8f74\u8868\u793a\u79fb\u9664\u8282\u70b9\u7684\u6bd4\u4f8b\u3002\u590d\u6742\u7f51\u7edc\u529f\u80fd\u53c8\u53ef\u4ee5\u4f7f\u7528\u7ed3\u6784\u6027\u529f\u80fd\u6307\u6807<\/span>\uff08\u5305\u62ec\u6700\u5927\u8fde\u901a\u7247\u89c4\u6a21\u7b49\uff09<\/span>\u548c\u975e\u7ed3\u6784\u6027\u529f\u80fd\u6307\u6807<\/span>\uff08\u5982\u519b\u4e8b\u4e2d\u7684\u4f53\u7cfb\u9884\u8b66\u89c4\u6a21\u7b49\uff09<\/span>\u6765\u8861\u91cf\u3002<\/span><\/section>\n

<\/section>\n
FINDER\u7b97\u6cd5\u7684\u6846\u67b6\u540c\u6837\u91c7\u53d6\u4e86\u7c7b\u4f3cS2V-DQN\u8fd9\u79cd\u7ed3\u5408\u56fe\u8868\u793a\u5b66\u4e60\u548c\u5f3a\u5316\u5b66\u4e60\u7684\u7b97\u6cd5\u6846\u67b6\uff0c\u4f46\u662f\u5b58\u5728\u4e00\u4e9b\u4e0d\u540c\uff0c\u533a\u522b\u5982\u4e0b\u6240\u793a:<\/span><\/section>\n

<\/span><\/section>\n\n\n\n\n\n\n\n\n
\n
\u4e3b\u8981\u533a\u522b<\/span><\/section>\n<\/td>\n
\n
S2V-DQN<\/strong><\/span><\/section>\n<\/td>\n
\n
FINDER<\/strong><\/span><\/section>\n<\/td>\n<\/tr>\n
\n
\u8282\u70b9\u8868\u793a<\/span><\/section>\n<\/td>\n
\n
Struct2Vec<\/span><\/section>\n<\/td>\n
\n
GraphSAGE<\/span><\/section>\n<\/td>\n<\/tr>\n
\n
\u72b6\u6001\u8868\u793a<\/span><\/section>\n<\/td>\n
\n
\u7b80\u5355\u7684\u8282\u70b9\u5d4c\u5165\u4e4b\u548c\u8868\u793a\u72b6\u6001<\/span><\/section>\n<\/td>\n
\n
\u5f15\u5165\u865a\u62df\u8282\u70b9\u8868\u793a\u72b6\u6001<\/span><\/section>\n<\/td>\n<\/tr>\n
\n
\u52a8\u4f5c\u503c\u51fd\u6570 Q \u8ba1\u7b97<\/span><\/section>\n<\/td>\n
\n
\u72b6\u6001\u548c\u52a8\u4f5c\u5411\u91cf\u7684\u62fc\u63a5<\/span><\/section>\n<\/td>\n
\n
\u72b6\u6001\u548c\u52a8\u4f5c\u5411\u91cf\u7684\u4ea4\u53c9\u79ef<\/span><\/section>\n<\/td>\n<\/tr>\n
\n
\u8bad\u7ec3\u635f\u5931\u51fd\u6570<\/span><\/section>\n<\/td>\n
\n
\u8d1d\u5c14\u66fc\u635f\u5931<\/span><\/section>\n<\/td>\n
\n
\u8d1d\u5c14\u66fc\u635f\u5931\u52a0\u4e0a\u56fe\u91cd\u6784\u635f\u5931<\/span><\/section>\n<\/td>\n<\/tr>\n
\n
\u6d4b\u8bd5\u7b56\u7565<\/span><\/section>\n<\/td>\n
\n
\u4e00\u6b65\u6d4b\u8bd5\u9009\u62e9\u4e00\u4e2a\u8282\u70b9<\/span><\/section>\n<\/td>\n
\n
\u4e00\u6b65\u6d4b\u8bd5\u9009\u62e9\u4e00\u6279\u8282\u70b9<\/span><\/section>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

<\/section>\n
\u5bf9\u4e8e\u5b9e\u9a8c\u90e8\u5206\uff0c\u4f5c\u8005\u4f7f\u7528\u4e24\u79cd\u8fde\u901a\u6027\u7684\u8ba1\u7b97\u65b9\u6cd5<\/span>\uff08CN\uff0cN<\/span>D\uff09<\/span>\u4ee5\u53ca\u4e09\u79cd\u8282\u70b9\u8d4b\u6743\u65b9\u5f0f<\/span>\uff08\u65e0\u6743\u3001\u5ea6\u6743\u4ee5\u53ca\u968f\u673a\u6743\uff09<\/span>\uff0c\u5171\u516d\u79cd\u5b9e\u9a8c\u573a\u666f\u3002\u5728\u516d\u79cd\u4e0d\u540c\u7684\u7f51\u7edc\u74e6\u89e3\u573a\u666f\u4e2d\u6548\u679c\u5e73\u5747\u63d0\u534728.7%\u3002\u6b64\u5916\uff0c\u8be5\u65b9\u6cd5\u5177\u6709\u5f88\u597d\u7684\u6cdb\u5316\u80fd\u529b\uff0c\u53ef\u4ee5\u4f7f\u7528\u5c0f\u89c4\u6a21\u6a21\u62df\u56fe\u4e0a\u8bad\u7ec3\u7684\u6a21\u578b\u6765\u6d4b\u8bd5\u5927\u89c4\u6a21\u771f\u5b9e\u7684\u7f51\u7edc\uff0c\u8fd9\u8bf4\u660e\u6a21\u578b\u5b66\u5230\u4e86\u7f51\u7edc\u74e6\u89e3\u7684\u5185\u5728\u7684\u7b56\u7565\u3002\u540c\u65f6FINDER\u7684\u8ba1\u7b97\u6548\u7387\u4e5f\u63d0\u5347\u8d85\u8fc7\u4e24\u4e2a\u6570\u91cf\u7ea7\uff0cFINDER\u7b97\u6cd5\u81ea\u5df1\u53d1\u73b0\u4e86\u66f4\u5feb\u74e6\u89e3\u590d\u6742\u7f51\u7edc\u7684\u7b56\u7565\u3002\u5728\u52a0\u6743\u7f51\u7edc\u4e2d\uff0cFINDER\u4f18\u5148\u9009\u62e9cost-effective\u7684\u8282\u70b9\uff0c\u5373\u4ee3\u4ef7\u4e0d\u90a3\u4e48\u5927\uff0c\u5374\u80fd\u8fbe\u5230\u76f8\u540c\u7684\u74e6\u89e3\u6548\u679c\u3002<\/span><\/section>\n

<\/section>\n
DIRAC<\/strong><\/span><\/section>\n
\n
\n
\n
Fan C, Shen M, Nussinov Z, et al. Searching for spin glass ground states through deep reinforcement learning[J]. Nature Communications, 2023, 14(1): 725.<\/span><\/section>\n

<\/section>\n
\u8bba\u6587\u89e3\u8bfb\uff1aNat. Commun. \u524d\u6cbf\uff1a\u6df1\u5ea6\u5f3a\u5316\u5b66\u4e60\u641c\u7d22\u81ea\u65cb\u73bb\u7483\u57fa\u6001<\/a><\/a><\/em><\/span><\/section>\n<\/section>\n<\/section>\n<\/blockquote>\n

<\/section>\n
\u8be5\u5de5\u4f5c\u5c1d\u8bd5\u63d0\u51fa\u4e00\u79cd\u5f3a\u5316\u5b66\u4e60\u65b9\u6cd5<\/span>\uff08DIRAC\uff09<\/span>\u6765\u89e3\u51b3\u81ea\u65cb\u73bb\u7483\u7684\u57fa\u6001\u6c42\u89e3\u95ee\u9898\uff0c\u786e\u5b9a\u6700\u4f18\u7684\u8282\u70b9\u72b6\u6001\u4f7f\u5f97\u76ee\u6807\u51fd\u6570\u503c\u6700\u4f4e\uff0c\u5176\u4e2d\u76ee\u6807\u51fd\u6570\u4e3a<\/span><\/sub>\u8868\u793a\u8282\u70b9i\u7684\u72b6\u6001\uff0c\u6bcf\u6761\u8fb9\u7684\u6743\u91cd<\/span><\/sub>\uff0c\u76f4\u7ebf\u8868\u793a\u6b63\u6743\u8fb9\uff0c\u6298\u7ebf\u8868\u793a\u8d1f\u6743\u8fb9\uff0c\u8fb9\u7684\u5bbd\u5ea6\u4e0e<\/span><\/sub>\u5448\u6b63\u6bd4\uff0c\u84dd\u8272\u9634\u5f71\u90e8\u5206\u8868\u793a\u8fb9\u7684\u80fd\u91cf<\/span><\/sub>\u3002<\/span><\/section>\n

<\/section>\n
\u8be5\u65b9\u6cd5\u540c\u6837\u57fa\u4e8e\u56fe\u8868\u793a\u5b66\u4e60\u4e0e\u5f3a\u5316\u5b66\u4e60\u6846\u67b6\uff0c\u5176\u4e2d\u6bcf\u4e2a\u90e8\u5206\u90fd\u6709\u5c0f\u7684\u6539\u52a8\u4ee5\u9002\u7528\u81ea\u65cb\u73bb\u7483\u7684\u57fa\u6001\u6c42\u89e3\u95ee\u9898\u3002\u5176\u4e2d\uff0c\u57fa\u6001\u6c42\u89e3\u95ee\u9898\u53d7\u8fb9\u6743\u7684\u5f71\u54cd\u5f88\u5927\uff0c\u8fb9\u6743\u76f4\u63a5\u5f71\u54cd\u6700\u7ec8\u57fa\u6001\u7684\u5206\u5e03\uff0c\u6240\u4ee5\u8fd9\u91cc\u91c7\u7528\u4e24\u9636\u6bb5\u7684\u5d4c\u5165\u65b9\u5f0f\uff0c\u9996\u5148\u57fa\u4e8e\u4e24\u7aef\u8282\u70b9\u7684\u5d4c\u5165\u5411\u91cf\u66f4\u65b0\u8fb9\u7684\u5d4c\u5165\u5411\u91cf\uff0c\u7136\u540e\u518d\u57fa\u4e8e\u76f8\u90bb\u7684\u8fb9\u7684\u8868\u793a\u66f4\u65b0\u8282\u70b9\u7684\u8868\u793a\uff0c\u8fd9\u79cd\u65b9\u5f0f\u80fd\u66f4\u597d\u5730\u6355\u6349\u8fb9\u7684\u7279\u5f81\u4e0e\u6743\u91cd\u3002<\/span><\/section>\n

<\/span><\/section>\n
\u6b64\u5916\uff0c\u6700\u5173\u952e\u7684\u662f\u6dfb\u52a0\u4e86\u89c4\u8303\u53d8\u6362\u7684\u6280\u5de7<\/span>\uff08Gauge transformation\uff09<\/span>\u3002\u5f15\u5165\u53d8\u6362\u5728\u4e8e\uff0c\u4f20\u7edf\u7684\u5f3a\u5316\u5b66\u4e60\u5de5\u4f5c\u6d41\u7a0b\u5b58\u5728\u7f3a\u70b9\uff0c\u53ea\u80fd\u4ece\u67d0\u4e2a\u72b6\u6001\u51fa\u53d1\uff0c\u9010\u6b65\u589e\u52a0\u5143\u7d20<\/span>\uff08\u52a8\u4f5c\u9009\u62e9\uff09<\/span>\u6784\u5efa\u89e3\u96c6\uff0c\u76f4\u5230\u6ee1\u8db3\u7ec8\u6b62\u6761\u4ef6\uff0c\u6bcf\u4e2a\u8282\u70b9\u9009\u62e9\u4e14\u53ea\u80fd\u9009\u62e9\u4e00\u6b21\uff0c\u4f46\u662f\uff0c\u8fd9\u79cd\u65b9\u5f0f\u6700\u5927\u7684\u95ee\u9898\u5728\u4e8e\u52a8\u4f5c\u4e0d\u53ef\u91cd\u590d\u9009\u62e9\uff0c\u5373\u53ea\u80fd\u4ea7\u751f\u5355\u4e2a\u201c\u6700\u4f73\u731c\u6d4b\u201d\u3002\u7136\u800c\uff0c\u7ec4\u5408\u4f18\u5316\u95ee\u9898\u5de8\u5927\u7684\u63a2\u7d22\u7a7a\u95f4\u8981\u6c42\u667a\u80fd\u4f53\u80fd\u591f\u91cd\u590d\u591a\u6b21\u201c\u8bd5\u9519\u63a2\u7d22\u201d\u4ee5\u907f\u514d\u9677\u5165\u5c40\u90e8\u6700\u4f18\uff0c\u8be5\u5de5\u4f5c\u63d0\u51fa\u7684\u89c4\u8303\u53d8\u6362\u80fd\u591f\u5c06\u4efb\u610f\u72b6\u6001\u8f6c\u5316\u4e3a\u6307\u5b9a\u7684\u72b6\u6001\uff0c\u4ece\u800c\u9002\u5408\u667a\u80fd\u4f53\u7684\u91cd\u590d\u63a2\u7d22\u3002\u89c4\u8303\u53d8\u6362\u5e26\u6765\u5982\u4e0b\u597d\u5904\uff1a1\uff09\u5141\u8bb8\u6a21\u578b\u5728\u4e00\u6b21\u63a2\u7d22\uff08DIRAC1<\/sup>\uff09\u7ed3\u675f\u540e\u63a5\u7740\u7ee7\u7eed\u63a2\u7d22\uff08DIRAC<\/span>m<\/sup>\uff09\uff1b2\uff09\u5141\u8bb8\u6a21\u578b\u4ece\u591a\u4e2a\u4e0d\u540c\u521d\u6001\u51fa\u53d1\uff0c\u4ece\u800c\u53d6\u5f97\u66f4\u4f18\u7684\u7ed3\u679c\uff1b3\uff09\u5141\u8bb8\u6a21\u578b\u548c\u4efb\u610f\u542f\u53d1\u5f0f\u7b97\u6cd5\u7ed3\u5408\uff0c\u4ece\u800c\u8fdb\u4e00\u6b65\u63d0\u9ad8\u542f\u53d1\u5f0f\u7b97\u6cd5\u7684\u7ed3\u679c\uff1b\u5bf9\u6027\u80fd\u7684\u63d0\u5347\u4f5c\u7528\u5f88\u5927\u3002<\/span><\/section>\n

<\/span><\/section>\n
<\/section>\n
\u56fe5 \u81ea\u65cb\u73bb\u7483\u7684\u57fa\u6001\u793a\u610f\u56fe<\/span><\/section>\n

<\/section>\n
\u5b9e\u9a8c\u7ed3\u679c\u4e0a\uff0c\u5728\u5927\u5c3a\u5bf8\u7cfb\u7edf\u4e2d<\/span>\uff08\u57fa\u6001\u672a\u77e5\uff09<\/span>\uff0cDIRAC \u76f8\u6bd4\u76ee\u524d\u6700\u5148\u8fdb\u7684\u9000\u706b\u7b97\u6cd5\uff0c\u53ef\u4ee5\u53d6\u5f97\u66f4\u4f4e\u7684\u80fd\u91cf\u3002\u5728\u5c0f\u5c3a\u5bf8\u7cfb\u7edf\u4e2d<\/span>\uff08\u57fa\u6001\u5df2\u77e5\uff09<\/span>\uff0cDIRAC \u5747\u80fd\u6c42\u51fa\u5168\u5c40\u6700\u4f18\u89e3\u3002\u6b64\u5916\uff0c\u901a\u8fc7\u6a21\u578b\u7684\u53ef\u89e3\u91ca\u5206\u6790\u53d1\u73b0\uff0c\u8be5\u6a21\u578b\u662f\u901a\u8fc7\u727a\u7272\u77ed\u671f\u6536\u76ca\uff0c\u83b7\u53d6\u957f\u8fdc\u6536\u76ca\uff0c\u8fd9\u4e5f\u662f\u5f3a\u5316\u5b66\u4e60\u7684\u4f18\u52bf\u6240\u5728\u3002<\/span><\/section>\n

<\/span><\/section>\n
\u8be5\u95ee\u9898\u662f\u63ed\u5f00\u81ea\u65cb\u73bb\u7483\u5947\u602a\u800c\u590d\u6742\u884c\u4e3a\u7684\u5173\u952e\uff0c\u6709\u52a9\u4e8e\u89e3\u51b3\u8bb8\u591a\u7ec4\u5408\u4f18\u5316\u96be\u9898\uff0c\u6709\u52a9\u4e8e\u795e\u7ecf\u7f51\u7edc\u4f18\u5316\u5de5\u5177\u7684\u5f00\u53d1\u3002\u8be5\u7b97\u6cd5\u5177\u6709\u4ee5\u4e0b\u4f18\u70b9\uff1a1\uff09\u7cbe\u5ea6\u9ad8\uff0c\u4e2d\u7b49\u5c3a\u5bf8\u7684\u7cfb\u7edf\u53ef\u4ee5\u8fbe\u5230\u5168\u5c40\u6700\u4f18\u89e3\uff0c\u4e14\u9002\u7528\u4e8e\u4e09\u7ef4\u53ca\u4ee5\u4e0a\u7684\u9ad8\u7ef4\u7cfb\u7edf\uff1b2\uff09\u53ef\u6269\u5c55\u6027\u5f3a\uff0c\u6700\u5927\u7cfb\u7edf\u5c3a\u5bf8\u8fbe\u5230\u4e09\u7ef4\u7684 L=20\uff0c\u8fd9\u662f\u76ee\u524d\u7814\u7a76\u6700\u5927\u7684\u7cfb\u7edf\uff1b3\uff09\u53ef\u4ee5\u548c\u4efb\u610f\u9000\u706b\u7b97\u6cd5\u7b49\u542f\u53d1\u5f0f\u7b97\u6cd5\u7ed3\u5408\uff0c\u5e76\u8fdb\u4e00\u6b65\u63d0\u9ad8\u8fd9\u4e9b\u7b97\u6cd5\u7684\u8868\u73b0\u3002<\/span><\/section>\n

<\/span><\/section>\n
\u603b\u4e4b\u81ea\u56de\u5f52\u65b9\u6cd5\u4e2d\u57fa\u4e8e\u56fe\u8868\u793a\u5b66\u4e60\u52a0\u4e0a\u5f3a\u5316\u5b66\u4e60\u662f\u76ee\u524d\u4e00\u79cd\u4e3b\u6d41\u7684\u7814\u7a76\u8303\u5f0f\uff0c\u4f18\u70b9\u5728\u4e8e\u6a21\u578b\u6cdb\u5316\u6027\u80fd\u597d\u3001\u6a21\u578b\u6548\u679c\u597d\u4e14\u5177\u6709\u4e00\u5b9a\u7684\u53ef\u89e3\u91ca\u6027\u3002\u4f46\u662f\u5b58\u5728\u6a21\u578b\u8bbe\u8ba1\u3001\u5b66\u4e60\u96be\u4ee5\u53ca\u5b9e\u73b0\u56f0\u96be\u7b49\u7f3a\u70b9\u3002\u56e0\u6b64\uff0c\u4f5c\u8005\u4e5f\u6b63\u5728\u5f00\u53d1\u4e00\u79cd\u57fa\u4e8e\u56fe\u8868\u793a\u5b66\u4e60\u548c\u6df1\u5ea6\u5f3a\u5316\u5b66\u4e60\u7684\u56fe\u4e0a\u7ec4\u5408\u4f18\u5316\u95ee\u9898\u6c42\u89e3\u5de5\u5177<\/span>\uff08OptiGNN\uff09<\/span>\u6765\u964d\u4f4e\u6211\u4eec\u7684\u5b66\u4e60\u95e8\u69db\u3002<\/span><\/section>\n

<\/section>\n

<\/strong><\/span><\/section>\n

\n
\n
\n
\n
\n

<\/section>\n

<\/section>\n
\n
<\/svg><\/section>\n<\/section>\n<\/section>\n
\n

\u4e09\u3001\u7814\u7a76\u8fdb\u5c55<\/strong><\/strong><\/strong><\/p>\n<\/section>\n

\n

<\/section>\n

<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/h3>\n
 <\/span><\/section>\n
1. \u5982\u4f55\u4e0e\u7cbe\u786e\u7b97\u6cd5\u7ed3\u5408\uff1f<\/span><\/strong><\/section>\n

<\/section>\n
\n
\n
\n
Solving Mixed Integer Programs Using Neural Networks<\/span><\/section>\n
https:\/\/arxiv.org\/abs\/2012.13349<\/span><\/section>\n<\/section>\n<\/section>\n<\/blockquote>\n

<\/section>\n
\u5bf9\u4e8e\u6c42\u89e3\u6df7\u5408\u6574\u6570\u89c4\u5212\u95ee\u9898<\/span>\uff08MIP\uff09<\/span>\uff0c\u6700\u5e38\u89c1\u7684\u7cbe\u786e\u7b97\u6cd5\u5c31\u662f\u5206\u652f\u5b9a\u754c\u7b97\u6cd5\uff0c\u7136\u800c\u5bf9\u4e8e\u5177\u4f53\u5982\u4f55\u5206\u652f\u8ba1\u7b97\u662f\u5f88\u590d\u6742\u7684\uff0c\u4f20\u7edf\u7684\u65b9\u6cd5\u90fd\u662f\u628a\u5b83\u677e\u5f1b\u5230\u7ebf\u6027\u89c4\u5212\uff0c\u7136\u540e\u7528\u7ebf\u6027\u89c4\u5212\u6c42\u8fb9\u754c\uff0c\u518d\u9009\u62e9\u76f8\u5e94\u7684\u503c\u3002\u7136\u800c\u8fd9\u6837\u7684\u65b9\u6cd5\u8ba1\u7b97\u91cf\u5c31\u975e\u5e38\u5927\u3002\u73b0\u5728\u53ef\u4ee5\u4f7f\u7528\u673a\u5668\u5b66\u4e60\u65b9\u6cd5\u628a\u5206\u652f\u95ee\u9898\u770b\u6210\u662f\u4e00\u4e2a\u4e8c\u5206\u7c7b\u95ee\u9898\u3002\u5982 DeepMind \u63d0\u51fa\u7528\u795e\u7ecf\u7f51\u7edc\u6c42\u89e3\u6df7\u5408\u6574\u6570\u89c4\u5212\uff0c\u5305\u62ec Neural Diving \u4ee5\u53ca Neural Branching \u4e24\u4e2a\u90e8\u5206\uff0cNeural Diving \u8bad\u7ec3\u4e00\u4e2a\u6df1\u5ea6\u795e\u7ecf\u7f51\u7edc\u6765\u4ea7\u751fMIP\u95ee\u9898\u90e8\u5206\u6574\u6570\u53d8\u91cf\u7684\u8d4b\u503c\uff0c\u5269\u4f59\u672a\u8d4b\u503c\u7684\u53d8\u91cf\u5b9a\u4e49\u4e86\u4e00\u4e2a\u66f4\u5c0f\u7684\u201c\u5b50MIP\u201d\uff0c\u8fd9\u4e2a\u5b50\u95ee\u9898\u4f7f\u7528\u73b0\u6210\u7684MIP\u6c42\u89e3\u5668<\/span>\uff08\u5982SCIP\uff09<\/span>\u6765\u6c42\u89e3\uff0c\u8fdb\u800c\u4ea7\u751f\u4e00\u4e2a\u9ad8\u8d28\u91cf\u7684\u8054\u5408\u53d8\u91cf\u8d4b\u503c\uff1bNeural Branching \u8bad\u7ec3\u4e00\u4e2a\u6df1\u5ea6\u795e\u7ecf\u7f51\u7edc\uff0c\u5728\u7ecf\u5178\u6c42\u89e3\u5668\u8fdb\u884c\u5206\u652f\u5b9a\u754c\u7684\u8fc7\u7a0b\u4e2d\uff0c\u9488\u5bf9\u7ed9\u5b9a\u8282\u70b9\u6a21\u4eff\u4e13\u5bb6\u7684\u5206\u652f\u53d8\u91cf\u9009\u62e9\u7b56\u7565\u8fdb\u884c\u5206\u652f\u53d8\u91cf\u7684\u9009\u62e9\u3002<\/span><\/section>\n

<\/span><\/section>\n
\u5177\u4f53\u6765\u8bf4\uff0c\u53ef\u4ee5\u5c06MIP\u5efa\u6a21\u4e3a\u4e00\u4e2a\u4e8c\u90e8\u56fe\uff0c\u51b3\u7b56\u53d8\u91cf\u548c\u7ea6\u675f\u770b\u6210\u56fe\u4e2d\u7684\u4e24\u7c7b\u8282\u70b9\uff0c\u5176\u4e2d\u53d8\u91cf\u524d\u7684\u7cfb\u6570\u548c\u7ea6\u675f\u4f5c\u4e3a\u8282\u70b9\u548c\u8fb9\u7684\u7279\u5f81\u3002\u7136\u540e\u4f7f\u7528\u56fe\u795e\u7ecf\u7f51\u7edc\u751f\u6210\u90e8\u5206\u89e3\uff0c\u5177\u4f53\u6765\u8bf4\u53ef\u4ee5\u5c06\u6784\u5efa\u7684\u4e8c\u90e8\u56fe\u8f93\u5165\u56fe\u5377\u79ef\u795e\u7ecf\u7f51\u7edc\u5f97\u5230\u6bcf\u4e2a\u8282\u70b9\u7684\u8868\u793a\u5411\u91cf\uff0c\u63a5\u7740\u7ecf\u8fc7MLP\u8f93\u51fa\u5404\u4e2a\u53d8\u91cf\u4e3a\u67d0\u4e2a\u503c\u7684\u6982\u7387\u3002\u6b64\u5916\uff0c\u5728\u5206\u652f\u5b9a\u754c\u8fc7\u7a0b\u4e2d\uff0c\u4e3a\u4e86\u63d0\u9ad8\u51b3\u7b56\u8d28\u91cf\uff0c\u4f7f\u7528\u6a21\u4eff\u5b66\u4e60\uff0c\u6765\u5b66\u4e60strong branching\u7684\u4e13\u5bb6\u7ecf\u9a8c\u3002\u6a21\u578b\u5728\u901f\u5ea6\u548c\u7cbe\u5ea6\u5747\u53d6\u5f97\u51fa\u8272\u7684\u8868\u73b0\u3002<\/span><\/section>\n

<\/span><\/section>\n
<\/section>\n
\u56fe6 \u795e\u7ecfMIP\u6c42\u89e3\u5668\u7b97\u6cd5\u6d41\u7a0b\u56fe<\/span><\/section>\n

<\/section>\n
2. \u5982\u4f55\u4e0e\u542f\u53d1\u5f0f\u7b97\u6cd5\u7ed3\u5408\uff1f<\/span><\/strong><\/span><\/section>\n
\n
\n
Mills, Kyle and Ronagh, Pooya and Tamblyn, Isaac. Finding the ground state of spin Hamiltonians with reinforcement learning. Nature Machine Intelligence, 2020.<\/span><\/section>\n<\/section>\n<\/blockquote>\n

<\/section>\n
\u8be5\u5de5\u4f5c\u5c1d\u8bd5\u7ed3\u5408\u542f\u53d1\u5f0f\u7b97\u6cd5\u89e3\u51b3\u81ea\u65cb\u73bb\u7483\u7684\u57fa\u6001\u6c42\u89e3\u95ee\u9898\uff0c\u6211\u4eec\u77e5\u9053\u5bf9\u4e8e\u6a21\u62df\u9000\u706b\u65b9\u6cd5\uff0c\u6709\u4e2a\u5173\u952e\u95ee\u9898\u5c31\u662f\u6e29\u5ea6\u5982\u4f55\u4e0b\u964d\uff0c\u4e3a\u4e86\u514b\u670d\u8fd9\u4e2a\u95ee\u9898\uff0c\u8be5\u65b9\u6cd5\u5229\u7528\u5f3a\u5316\u5b66\u4e60\u5b66\u4e60\u6a21\u62df\u9000\u706b\u7b97\u6cd5\u7684\u6700\u4f73\u9000\u706b\u7b56\u7565\uff0c\u7136\u540e\u4f7f\u7528\u6700\u4f73\u9000\u706b\u7b56\u7565\u6765\u6c42\u89e3\u81ea\u65cb\u73bb\u7483\u7684\u57fa\u6001\u3002\u7136\u800c\uff0c\u8be5\u65b9\u6cd5\u7684\u5b9e\u9a8c\u7ed3\u679c\u5e76\u6ca1\u6709\u90a3\u4e48\u597d\u3002<\/span><\/section>\n

<\/span><\/section>\n
<\/section>\n
\u56fe7 \u57fa\u4e8e\u5f3a\u5316\u5b66\u4e60\u5bfb\u627e\u81ea\u65cb\u54c8\u5bc6\u987f\u91cf\u7684\u57fa\u6001\u7b97\u6cd5\u6d41\u7a0b\u56fe<\/span><\/section>\n

<\/section>\n
3. \u5982\u4f55\u63d0\u9ad8\u5728\u5927\u89c4\u6a21\u5b9e\u4f8b\u7684\u6cdb\u5316\u8868\u73b0\uff1f<\/span><\/strong><\/span><\/section>\n
\n
\n
\n
Generalize a Small Pre-trained Model to Arbitrarily Large TSP Instances<\/span><\/section>\n
https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/view\/16916<\/span><\/section>\n<\/section>\n<\/section>\n<\/blockquote>\n
<\/span><\/section>\n

<\/section>\n
\u5bf9\u4e8e\u5982\u4f55\u63d0\u9ad8\u5927\u6a21\u578b\u5b9e\u4f8b\u7684\u6cdb\u5316\u80fd\u529b\uff0c\u4e0b\u9762\u8bb2\u89e3\u4e24\u4e2a\u5de5\u4f5c\uff1a<\/span><\/section>\n

<\/span><\/section>\n
\u7b2c\u4e00\u4e2a\u5de5\u4f5c\u4f7f\u7528\u76d1\u7763\u5b66\u4e60\u89e3\u51b3\u7ecf\u5178\u7684TSP\u95ee\u9898\uff0c\u6587\u7ae0\u63d0\u51fa\u7684\u662f\u4e00\u79cd\u6df7\u5408\u7b97\u6cd5\uff0c\u7ea7\u8054\u4e86SL\u548cRL\u6a21\u5757\u3002SL\u6a21\u5757\u8fd0\u7528\u9884\u8bad\u7ec3\u7684\u6a21\u578b\u4e0e\u4e09\u79cd\u56fe\u6280\u5de7<\/span>\uff08\u5305\u62ec\u56fe\u91c7\u6837\u3001\u56fe\u8f6c\u6362\u3001\u56fe\u5408\u5e76\uff09<\/span>\u4e3a\u4efb\u610f\u89c4\u6a21\u7684TSP\u6784\u5efa\u70ed\u529b\u56fe\u3002\u70ed\u529b\u56fe\u523b\u753b\u4e86\u8282\u70b9\u4e4b\u95f4\u6bcf\u6761\u8fb9\u5c5e\u4e8e\u8be5TSP\u6700\u4f18\u73af\u6e38\u7684\u6982\u7387\u3002\u968f\u540e\uff0c\u70ed\u529b\u56fe\u5c06\u4f5c\u4e3aRL\u6a21\u5757\u7684\u91cd\u8981\u8f93\u5165\u3002RL\u7b97\u6cd5\u5c06\u8fdb\u4e00\u6b65\u4f7f\u7528\u8499\u7279\u5361\u6d1b\u641c\u7d22\u5bfb\u627e\u6700\u4f18\u89e3\uff0c\u8f93\u51faTSP\u7684\u6700\u4f18\u73af\u6e38\u3002\u8be5\u5de5\u4f5c\u5177\u6709\u5f88\u597d\u7684\u6cdb\u5316\u80fd\u529b\u5177\u4f53\u4f53\u73b0\u5728\u5982\u4e0b\u64cd\u4f5c\uff0c\u8bad\u7ec3\u4e00\u4e2a\u5c0f\u89c4\u6a21\u7f51\u7edc\u7684\u9884\u8bad\u7ec3\u6a21\u578b\uff0c\u5bf9\u4e8e\u4efb\u610f\u5927\u89c4\u6a21\u7684\u56fe\u8f93\u5165\u90fd\u53ef\u4ee5\u901a\u8fc7\u5bf9\u56fe\u8fdb\u884c\u91c7\u6837\u6784\u5efa\u5b50\u56fe\uff0c\u7136\u540e\u5206\u522b\u901a\u8fc7\u5b66\u4e60\u597d\u7684\u9884\u8bad\u7ec3\u6a21\u578b\uff0c\u6700\u540e\u5c06\u5b50\u56fe\u5408\u5e76\u6210\u539f\u59cb\u7684\u5927\u56fe\uff0c\u89e3\u51b3\u4e86\u8bad\u7ec3\u548c\u6d4b\u8bd5\u56fe\u7684\u5c3a\u5bf8\u4e0d\u4e00\u81f4\u7684\u60c5\u51b5\u3002\u4f46\u662f\u8be5\u65b9\u6cd5\u5e94\u7528\u4e5f\u6709\u9650\uff0c\u8be5\u65b9\u6cd5\u65e0\u6cd5\u4e0e\u5f3a\u5316\u5b66\u4e60\u65b9\u6cd5\u8fdb\u884c\u7ed3\u5408\u3002<\/span><\/section>\n

<\/span><\/section>\n
<\/section>\n
\u56fe8 \u89e3\u51b3TSP\u7b97\u6cd5\u7684\u6d41\u7a0b\u56fe<\/span><\/section>\n

<\/section>\n
\u7b2c\u4e8c\u4e2a\u5de5\u4f5c\u662f\u524d\u6587\u63d0\u5230\u7684 FINDER \u7b97\u6cd5\u6846\u67b6\u3002\u5728FINDER\u65b9\u6cd5\u4e2d\uff0c\u4f5c\u8005\u4e5f\u5c1d\u8bd5\u4f7f\u7528\u914d\u7f6e\u6a21\u578b\u6765\u63d0\u9ad8\u6a21\u578b\u7684\u6cdb\u5316\u80fd\u529b\uff0c\u5373\u5b9a\u5236\u5339\u914d\u76ee\u6807\u7f51\u7edc\u7684\u8bad\u7ec3\u6570\u636e\uff0c\u53ef\u4ee5\u6839\u636e\u76ee\u6807\u7f51\u7edc\u7684\u5ea6\u5206\u5e03\u91c7\u6837\u6784\u5efa\u4e00\u4e2a\u5c0f\u89c4\u6a21\u56fe\uff0c\u7136\u540e\u5728\u8fd9\u4e9b\u751f\u6210\u7684\u56fe\u4e0a\u8bad\u7ec3\u6a21\u578b\u3002\u5b9e\u9a8c\u53d1\u73b0\u8be5\u65b9\u6cd5\u80fd\u63d0\u5347\u4e00\u4e9b\u6027\u80fd\u4f46\u662f\u63d0\u5347\u4e0d\u662f\u5f88\u5927\uff0c\u540c\u65f6\u9488\u5bf9\u7279\u5b9a\u7684\u7f51\u7edc\u90fd\u8981\u91cd\u65b0\u8bad\u7ec3\u589e\u52a0\u4e86\u6a21\u578b\u8ba1\u7b97\u91cf\u3002<\/span><\/section>\n

<\/span><\/section>\n
\u603b\u4e4b\uff0c\u8fd9\u4e24\u4e2a\u5de5\u4f5c\u4e00\u5b9a\u7a0b\u5ea6\u4e0a\u63d0\u5347\u4e86\u5927\u6a21\u578b\u7684\u6cdb\u5316\u80fd\u529b\uff0c\u4f46\u662f\u4e5f\u5e26\u6765\u4e86\u8ba1\u7b97\u91cf\u7684\u589e\u52a0\u7b49\u95ee\u9898\uff0c\u9700\u8981\u8fdb\u4e00\u6b65\u7684\u5b8c\u5584\u3002<\/span><\/section>\n

<\/section>\n
4. \u5982\u4f55\u5904\u7406\u590d\u6742\u7684\u7ea6\u675f\uff1f<\/span><\/strong><\/span><\/section>\n
\n
\n
\n
DC3: A learning method for optimization with hard constraints<\/span><\/section>\n
https:\/\/arxiv.org\/abs\/2104.12225<\/span><\/section>\n<\/section>\n<\/section>\n<\/blockquote>\n

<\/section>\n
DC3\u8fd9\u4e2a\u5de5\u4f5c\u5c1d\u8bd5\u89e3\u51b3\u5e26\u6709\u590d\u6742\u7ea6\u675f\u7684\u4f18\u5316\u95ee\u9898\uff0c\u8fd9\u91cc\u7684\u53d8\u91cf\u90fd\u662f\u8fde\u7eed\u7684\uff0c\u540c\u65f6\u5b58\u5728\u7b49\u5f0f\u7ea6\u675f\u4ee5\u53ca\u4e0d\u7b49\u5f0f\u7ea6\u675f\u3002\u5177\u4f53\u6765\u8bf4\uff0c\u5982\u679c\u51b3\u7b56\u53d8\u91cf\u6709N\u4e2a\uff0c\u540c\u65f6\u5b58\u5728M\u4e2a\u7b49\u5f0f\u7ea6\u675f\uff0c\u90a3\u4e48\u4e00\u5f00\u59cb\u6211\u4eec\u4f7f\u7528\u795e\u7ecf\u7f51\u7edc\u5148\u6c42\u5f97N-M\u4e2a\u7b26\u5408\u8981\u6c42\u7684\u89e3\uff0c\u7136\u540e\u4f7f\u7528\u7b49\u5f0f\u7ea6\u675f\u6c42\u51fa\u5176\u4f59\u7684\u53d8\u91cf\u503c\uff0c\u540c\u65f6\u4f7f\u7528\u4e0d\u7b49\u5f0f\u7ea6\u675f\u6765\u4fee\u6b63\u5f97\u5230\u7684\u89e3\uff0c\u6700\u540e\u8ba1\u7b97\u635f\u5931\u51fd\u6570\u53cd\u5411\u66f4\u65b0\u53c2\u6570\u6765\u4f18\u5316\u6a21\u578b\uff0c\u5176\u4e2d\u635f\u5931\u51fd\u6570\u5177\u4f53\u5305\u62ec\u4e09\u90e8\u5206\uff1a\u76ee\u6807\u51fd\u6570\u3001\u4e0d\u7b49\u5f0f\u7ea6\u675f\u4ee5\u53ca\u7b49\u5f0f\u7ea6\u675f\u635f\u5931\u3002\u8be5\u65b9\u6cd5\u7684\u4f18\u70b9\u5728\u4e8e\u8ba1\u7b97\u65f6\u95f4\u975e\u5e38\u5feb\uff0c\u7f3a\u70b9\u5728\u4e8e\u76ee\u524d\u7684\u7ea6\u675f\u8fd8\u662f\u505c\u7559\u5728\u7ebf\u6027\u7ea6\u675f\u4e0a\u9762\uff0c\u5982\u4f55\u62d3\u5c55\u5230\u975e\u7ebf\u6027\u7ea6\u675f\u4e5f\u662f\u4e00\u5927\u6311\u6218\u3002<\/span><\/section>\n

<\/section>\n
<\/section>\n
\u56fe9 DC3\u6846\u67b6\u793a\u610f\u56fe<\/span><\/section>\n

<\/section>\n
5. \u5982\u4f55\u4e0e\u6210\u719f\u7684\u6c42\u89e3\u5668\u7ed3\u5408\uff1f<\/strong><\/span><\/section>\n

<\/section>\n
\u5bf9\u4e8e\u4e0e\u6210\u719f\u7684\u6c42\u89e3\u5668\u7ed3\u5408\u7ed3\u5408\u89e3\u51b3\u7ec4\u5408\u4f18\u5316\u95ee\u9898\u4e5f\u662f\u4e00\u5927\u7814\u7a76\u70ed\u70b9\uff0c\u4e0b\u9762\u5177\u4f53\u4ecb\u7ecd\u4e24\u4e2a\u5de5\u4f5c\uff1a<\/span><\/section>\n
\n
\n
\n
Differentiation of Blackbox Combinatorial Solvers<\/span><\/section>\n
https:\/\/openreview.net\/forum?id=BkevoJSYPB<\/span><\/section>\n<\/section>\n<\/section>\n<\/blockquote>\n

<\/section>\n
BlackBox \u8fd9\u4e2a\u5de5\u4f5c\u5c1d\u8bd5\u5c06\u7ec4\u5408\u4f18\u5316\u6c42\u89e3\u5668\u53d8\u6210\u6df1\u5ea6\u5b66\u4e60\u6a21\u578b\u4e2d\u7684\u53ef\u5fae\u6784\u5efa\u5757\u3002\u5177\u4f53\u6765\u8bf4\uff0c\u7531\u4e8e\u4e00\u4e2a\u8f93\u5165\u5982\u56fe\u50cf\u7b49\u7ecf\u8fc7\u795e\u7ecf\u7f51\u7edc\u4f1a\u5f97\u5230\u8fde\u7eed\u7684\u8868\u5f81\uff0c\u7136\u800c\u6c42\u89e3\u5668\u9700\u8981\u79bb\u6563\u7684\u8f93\u5165\u3002\u56e0\u6b64\uff0c\u8be5\u5de5\u4f5c\u7684\u91cd\u70b9\u662f\u8003\u8651\u5982\u4f55\u5c06\u8fde\u7eed\u7684\u8868\u5f81\u8f6c\u6362\u6210\u6c42\u89e3\u5668\u7684\u8f93\u5165\uff0c\u53cd\u5411\u68af\u5ea6\u66f4\u65b0\u65f6\u53c8\u80fd\u5c06\u6c42\u89e3\u5668\u7684\u79bb\u6563\u8f93\u51fa\u8f6c\u6362\u4e3a\u795e\u7ecf\u7f51\u7edc\u7684\u8fde\u7eed\u8f93\u5165\uff0c\u8bad\u7ec3\u4e00\u4e2a\u7aef\u5230\u7aef\u7684\u6846\u67b6\u3002\u8be5\u5de5\u4f5c\u7684\u4f18\u70b9\u5728\u4e8e\u5982\u679c\u9700\u8981\u6c42\u51fa\u56fe\u7247\u4e2d\u4e24\u4e2a\u70b9\u4e4b\u95f4\u7684\u8ddd\u79bb\uff0c\u53ea\u9700\u8981\u8f93\u5165\u4e00\u5f20\u56fe\u7247\uff0c\u4e0d\u9700\u8981\u5bf9\u56fe\u50cf\u8fdb\u884c\u6805\u683c\u5316\u7b49\u989d\u5916\u64cd\u4f5c\uff0c\u6a21\u578b\u76f4\u63a5\u8f93\u51fa\u4e24\u4e2a\u70b9\u4e4b\u95f4\u7684\u6700\u77ed\u8def\u5f84\u3002<\/span><\/section>\n

<\/span><\/section>\n
<\/section>\n
\u56fe10 \u9ed1\u76d2\u7ec4\u5408\u6c42\u89e3\u5668\u7b97\u6cd5\u6d41\u7a0b\u56fe<\/span><\/section>\n

<\/section>\n
\n
\n
\n
Network planning with deep reinforcement learning<\/span><\/section>\n
https:\/\/dl.acm.org\/doi\/10.1145\/3452296.3472902<\/span><\/section>\n<\/section>\n<\/section>\n<\/blockquote>\n

<\/section>\n
\u8be5\u5de5\u4f5c\u63d0\u51fa\u4e00<\/span>\u4e2a\u4e24\u9636\u6bb5\u7684\u4f18\u5316\u65b9\u6cd5\u5c1d\u8bd5\u7ed3\u5408\u6df1\u5ea6\u5f3a\u5316\u5b66\u4e60\u4e0e\u7cbe\u786e\u6c42\u89e3\u5668\uff08NeuroPlan<\/strong>\uff09\u6765\u89e3\u51b3<\/span>\u4e00\u4e2a\u8d44\u6e90\u8c03\u5ea6\u95ee\u9898\u3002\u4e3a\u4e86\u514b\u670d\u7cbe\u786e\u6c42\u89e3\u5668\u65e0\u6cd5\u6c42\u89e3\u5927\u89c4\u6a21\u7684\u4f18\u5316\u95ee\u9898\u7684\u56f0\u96be\uff0c\u4f5c\u8005\u5c06\u4f18\u5316\u8fc7\u7a0b\u5206\u4e3a\u4e24\u4e2a\u9636\u6bb5\uff1a\u7b2c\u4e00\u4e2a\u9636\u6bb5\u4f7f\u7528\u5f3a\u5316\u5b66\u4e60\u65b9\u6cd5\u5b66\u4e60\u4e00\u4e2a\u79bb\u6700\u4f18\u89e3\u8f83\u8fd1\u7684\u7ed3\u679c\uff0c\u7f29\u5c0f\u641c\u7d22\u7a7a\u95f4\u7684\u8303\u56f4\uff1b\u7b2c\u4e8c\u9636\u6bb5\u4f7f\u7528\u7cbe\u786e\u6c42\u89e3\u5668\u6c42\u5f97\u6700\u4f18\u89e3\u3002\u5728\u5c0f\u89c4\u6a21\u4f18\u5316\u4efb\u52a1\u4e2d\uff0c\u5bf9\u6bd4\u53ea\u4f7f\u7528\u5f3a\u5316\u5b66\u4e60\u7684\u65b9\u6cd5\uff0c\u8be5\u65b9\u6cd5\u80fd\u6709\u66f4\u597d\u7684\u4f18\u5316\u6548\u679c\uff0c\u540c\u65f6\u80fd\u5ab2\u7f8e\u7cbe\u786e\u6c42\u89e3\u5668\u65b9\u6cd5\u3002\u6b64\u5916\uff0c\u5728\u5927\u89c4\u6a21\u4f18\u5316\u4efb\u52a1\u4e2d\uff0c\u8be5\u65b9\u6cd5\u540c\u6837\u80fd\u53d6\u5f97\u4e86\u6700\u597d\u7684\u4f18\u5316\u6548\u679c\uff0c\u7136\u800c\u5bf9\u4e8e\u7cbe\u786e\u6c42\u89e3\u5668\u6765\u8bf4\uff0c\u8d85\u51fa\u4e86\u5176\u6c42\u89e3\u89c4\u6a21\u5bfc\u81f4\u65e0\u6cd5\u8ba1\u7b97\u3002<\/span><\/section>\n

<\/section>\n
<\/section>\n
\u56fe11 NeuroPlan\u7684\u4e24\u9636\u6bb5\u6df7\u5408\u65b9\u6cd5\u793a\u610f\u56fe<\/span><\/section>\n

<\/section>\n
6. \u5982\u4f55\u5904\u7406\u66f4\u81ea\u7136\u7684\u539f\u59cb\u8f93\u5165\uff1f<\/span><\/strong><\/span><\/section>\n

<\/section>\n
\u5728\u4e0e\u6210\u719f\u7684\u6c42\u89e3\u5668\u7ed3\u5408\u7684\u5de5\u4f5c\u4e2d\uff0c\u53ef\u4ee5\u5904\u7406\u56fe\u50cf\u8f93\u5165\uff0c\u90a3\u4e48\u968f\u7740ChatGPT\u7684\u51fa\u73b0\uff0c\u5176\u80fd\u591f\u7406\u89e3\u81ea\u7136\u8bed\u8a00\u7684\u95ee\u9898\u9700\u6c42\uff0c\u4f7f\u5f97\u4ee5\u540e\u6839\u636e\u95ee\u9898\u7406\u89e3\u76f4\u63a5\u81ea\u52a8\u5efa\u6a21\u5e76\u8c03\u7528\u6c42\u89e3\u5668\u6c42\u89e3\u6210\u4e3a\u4e86\u53ef\u80fd\u3002<\/span><\/section>\n
<\/span>
<\/section>\n

<\/section>\n
\u626b\u7801\u9605\u8bfb\u5e76\u6536\u85cf\u5b8c\u6574\u6591\u56fe\u8def\u5f84\uff1a<\/span><\/strong><\/span><\/section>\n
<\/section>\n
<\/span><\/section>\n
\u6591\u56fe\u94fe\u63a5\uff1ahttps:\/\/pattern.swarma.org\/article\/240<\/span><\/section>\n

<\/section>\n

<\/section>\n
\u8bb2\u8005\u4ecb\u7ecd<\/span><\/strong><\/span><\/section>\n
<\/section>\n
\u8303\u957f\u4fca\uff1a\u56fd\u9632\u79d1\u6280\u5927\u5b66\u7cfb\u7edf\u5de5\u7a0b\u5b66\u9662\u526f\u6559\u6388\u3002\u7814\u7a76\u65b9\u5411\u5305\u62ec\u56fe\u6df1\u5ea6\u5b66\u4e60\u3001\u7ec4\u5408\u4f18\u5316\u3001\u5f3a\u5316\u5b66\u4e60\u53ca\u5176\u5728\u667a\u80fd\u51b3\u7b56\u3001\u590d\u6742\u7cfb\u7edf\u548c\u6307\u6325\u63a7\u5236\u4e2d\u7684\u5e94\u7528\u3002\u4ee5\u7b2c\u4e00\u4f5c\u8005\u6216\u901a\u8baf\u4f5c\u8005\u5728Nature Machine Intelligence\u3001Nature Communications\u3001AAAI\u7b49\u9876\u7ea7\u671f\u520a\u548c\u4f1a\u8bae\u53d1\u8868\u8bba\u6587\u591a\u7bc7\u3002<\/span><\/section>\n

<\/section>\n

<\/section>\n
\n
\n
\n
\n

\u56fe\u795e\u7ecf\u7f51\u7edc\u4e0e\u7ec4\u5408\u4f18\u5316\u8bfb\u4e66\u4f1a\u542f\u52a8<\/strong><\/p>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n

\n


<\/p>\n<\/section>\n

\u73b0\u5b9e\u4e16\u754c\u4e2d\u5927\u91cf\u95ee\u9898\u7684\u89e3\u51b3\u4f9d\u8d56\u4e8e\u7b97\u6cd5\u7684\u8bbe\u8ba1\u4e0e\u6c42\u89e3\u3002\u4f20\u7edf\u7b97\u6cd5\u7531\u4eba\u7c7b\u4e13\u5bb6\u8bbe\u8ba1\uff0c\u800c\u968f\u7740\u4eba\u5de5\u667a\u80fd\u6280\u672f\u4e0d\u65ad\u53d1\u5c55\uff0c\u7b97\u6cd5\u81ea\u52a8\u5b66\u4e60\u7b97\u6cd5\u7684\u6848\u4f8b\u65e5\u76ca\u589e\u591a\uff0c\u5982\u4ee5\u795e\u7ecf\u7f51\u7edc\u4e3a\u4ee3\u8868\u7684\u7684\u4eba\u5de5\u667a\u80fd\u7b97\u6cd5\uff0c\u8fd9\u662f\u7b97\u6cd5\u795e\u7ecf\u5316\u6c42\u89e3\u7684\u7f18\u7531\u3002\u5728\u7b97\u6cd5\u795e\u7ecf\u5316\u6c42\u89e3\u65b9\u5411\u4e0a\uff0c\u56fe\u795e\u7ecf\u7f51\u7edc\u662f\u4e00\u4e2a\u5f3a\u6709\u529b\u7684\u5de5\u5177\uff0c\u80fd\u591f\u5145\u5206\u5229\u7528\u56fe\u7ed3\u6784\u7684\u7279\u6027\uff0c\u5b9e\u73b0\u5bf9\u9ad8\u590d\u6742\u5ea6\u7b97\u6cd5\u7684\u9ad8\u6548\u8fd1\u4f3c\u6c42\u89e3\u3002\u57fa\u4e8e\u56fe\u795e\u7ecf\u7f51\u7edc\u7684\u590d\u6742\u7cfb\u7edf\u4f18\u5316\u4e0e\u63a7\u5236\u5c06\u4f1a\u662f\u5927\u6a21\u578b\u70ed\u6f6e\u4e4b\u540e\u65b0\u7684\u672a\u6765\u65b9\u5411\u3002<\/span><\/section>\n

<\/span><\/section>\n
\u4e3a\u4e86\u63a2\u8ba8\u56fe\u795e\u7ecf\u7f51\u7edc\u5728\u7b97\u6cd5\u795e\u7ecf\u5316\u6c42\u89e3\u7684\u53d1\u5c55\u4e0e\u73b0\u5b9e\u5e94\u7528\uff0c\u96c6\u667a\u4ff1\u4e50\u90e8\u8054\u5408\u56fd\u9632\u79d1\u6280\u5927\u5b66\u7cfb\u7edf\u5de5\u7a0b\u5b66\u9662\u526f\u6559\u6388\u8303\u957f\u4fca\u3001\u4e2d\u56fd\u4eba\u6c11\u5927\u5b66\u9ad8\u74f4\u4eba\u5de5\u667a\u80fd\u5b66\u9662\u52a9\u7406\u6559\u6388\u9ec4\u6587\u70b3\uff0c\u5171\u540c\u53d1\u8d77\u300c\u56fe\u795e\u7ecf\u7f51\u7edc\u4e0e\u7ec4\u5408\u4f18\u5316\u300d\u8bfb\u4e66\u4f1a\u3002\u8bfb\u4e66\u4f1a\u5c06\u805a\u7126\u4e8e\u56fe\u795e\u7ecf\u7f51\u7edc\u4e0e\u7b97\u6cd5\u795e\u7ecf\u5316\u6c42\u89e3\u7684\u76f8\u5173\u9886\u57df\uff0c\u5305\u62ec\u795e\u7ecf\u7b97\u6cd5\u63a8\u7406\u3001\u7ec4\u5408\u4f18\u5316\u95ee\u9898\u6c42\u89e3\u3001\u51e0\u4f55\u56fe\u795e\u7ecf\u7f51\u7edc\uff0c\u4ee5\u53ca\u7b97\u6cd5\u795e\u7ecf\u5316\u6c42\u89e3\u5728 AI for Science \u4e2d\u7684\u5e94\u7528\u7b49\u65b9\u9762\uff0c\u5e0c\u671b\u4e3a\u53c2\u4e0e\u8005\u63d0\u4f9b\u4e00\u4e2a\u5b66\u672f\u4ea4\u6d41\u5e73\u53f0\uff0c\u6fc0\u53d1\u53c2\u4e0e\u8005\u7684\u5b66\u672f\u5174\u8da3\uff0c\u8fdb\u4e00\u6b65\u63a8\u52a8\u76f8\u5173\u9886\u57df\u7684\u7814\u7a76\u548c\u5e94\u7528\u53d1\u5c55\u3002\u8bfb\u4e66\u4f1a\u4ece2023\u5e746\u670814\u65e5\u5f00\u59cb\uff0c\u6bcf\u5468\u4e09\u665a 19:00-21:00 \u4e3e\u884c\uff0c\u6301\u7eed\u65f6\u95f4\u9884\u8ba18\u5468\u3002\u6b22\u8fce\u611f\u5174\u8da3\u7684\u670b\u53cb\u62a5\u540d\u53c2\u4e0e\uff01<\/span><\/section>\n

<\/span><\/section>\n
<\/span><\/a><\/section>\n
<\/span><\/section>\n
\u8be6\u60c5\u8bf7\u89c1\uff1a<\/span><\/span><\/section>\n
\u52a0\u901f\u7ecf\u5178\u7b97\u6cd5\u6548\u7387\uff0c\u7a81\u7834\u73b0\u5b9e\u6280\u672f\u74f6\u9888\uff1a\u56fe\u795e\u7ecf\u7f51\u7edc\u4e0e\u7ec4\u5408\u4f18\u5316\u8bfb\u4e66\u4f1a\u542f\u52a8<\/a><\/span><\/section>\n
\u5927\u6a21\u578b\u70ed\u6f6e\u4e4b\u540e\u7684\u672a\u6765\u65b0\u65b9\u5411\uff1a\u56fe\u795e\u7ecf\u7f51\u7edc\u4e0e\u7ec4\u5408\u4f18\u5316 | \u6587\u732e\u6c47\u603b<\/span><\/a>
<\/section>\n

<\/section>\n

<\/section>\n
\n

\u63a8\u8350\u9605\u8bfb<\/span><\/strong><\/strong><\/p>\n

1. <\/strong>\u89e3\u7801\u859b\u5b9a\u8c14\u7684\u732b\uff1a\u91cf\u5b50\u7269\u7406\u548c\u4eba\u5de5\u667a\u80fd\u7684\u65b0\u9896\u4ea4\u53c9<\/a><\/strong><\/span><\/section>\n
2.<\/strong> \u4eba\u5de5\u667a\u80fd\u7684\u9ece\u660e\uff1a\u4ece\u4fe1\u606f\u52a8\u529b\u5b66\u7684\u89d2\u5ea6\u770bChatGPT<\/a><\/strong><\/span><\/section>\n
<\/a><\/section>\n
3.<\/strong> \u667a\u80fd\u662f\u4ec0\u4e48\uff1f\u8303\u7574\u8bba\u4e3a\u901a\u7528\u4eba\u5de5\u667a\u80fd\u63d0\u4f9b\u666e\u9002\u6846\u67b6<\/strong><\/a><\/a><\/section>\n
4. \u300a\u5f20\u6c5f\u00b7\u590d\u6742\u79d1\u5b66\u524d\u6cbf27\u8bb2\u300b\u5b8c\u6574\u4e0a\u7ebf\uff01<\/strong><\/a><\/strong><\/strong><\/strong><\/section>\n
5. \u6210\u4e3a\u96c6\u667aVIP\uff0c\u89e3\u9501\u5168\u7ad9\u8bfe\u7a0b\uff0f\u8bfb\u4e66\u4f1a<\/span><\/strong><\/a><\/strong><\/strong><\/strong><\/strong><\/section>\n
6. \u52a0\u5165\u96c6\u667a\uff0c\u4e00\u8d77\u590d\u6742\uff01<\/strong><\/a><\/strong><\/strong><\/strong><\/section>\n


<\/p>\n


<\/p>\n<\/section>\n

\u70b9\u51fb\u201c\u9605\u8bfb\u539f\u6587\u201d\uff0c\u89c2\u770b\u8bfb\u4e66\u4f1a\u56de\u653e<\/span><\/strong><\/span><\/strong><\/section>\n

<\/mp-style-type><\/p>\n<\/div>\n","protected":false},"excerpt":{"rendered":"

\u5bfc\u8bed \u73b0\u5b9e\u4e16\u754c\u4e2d\u5927\u91cf\u95ee\u9898\u7684\u89e3\u51b3\u9700\u8981\u8bbe\u8ba1\u548c\u6c42\u89e3\u7b97\u6cd5\u3002\u4f20\u7edf\u4e0a\u901a\u5e38\u662f\u4eba\u7c7b\u5b66\u4e60\u5982\u4f55\u8bbe\u8ba1\u7b97\u6cd5\uff0c\u800c\u968f\u7740\u4eba\u5de5\u667a\u80fd\u6280\u672f\u7684\u53d1\u5c55\uff0c\u6211\u4eec\u4e5f\u53ef\u4ee5\u901a\u8fc7\u673a\u5668\u5b66\u4e60\u65b9\u6cd5\u53bb\u5b66\u4e60\u5982\u4f55\u8bbe\u8ba1\u7b97\u6cd5\u3002\u5bf9\u4e8e\u7ec4\u5408\u4f18\u5316\u95ee\u9898\uff0c\u673a\u5668\u5b66\u4e60\u7b97\u6cd5\u80fd\u591f\u6c42\u89e3\u8d85\u5927\u89c4\u6a21\u7684\u590d\u6742\u7cfb\u7edf\u95ee\u9898\uff0c\u5177\u6709\u5f3a\u5927\u7684\u4f18\u52bf\u3002 \u5728\u300c\u56fe\u795e\u7ecf\u7f51\u7edc\u4e0e\u7ec4\u5408\u4f18\u5316\u300d\u8bfb\u4e66\u4f1a\u7b2c\u4e00\u671f\u5206\u4eab\u4e2d\uff0c\u56fd\u9632\u79d1\u6280\u5927\u5b66\u7cfb\u7edf\u5de5…<\/p>\n","protected":false},"author":0,"featured_media":43402,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":[],"categories":[1],"tags":[],"special":[],"_links":{"self":[{"href":"https:\/\/swarma.org\/index.php?rest_route=\/wp\/v2\/posts\/43425"}],"collection":[{"href":"https:\/\/swarma.org\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/swarma.org\/index.php?rest_route=\/wp\/v2\/types\/post"}],"replies":[{"embeddable":true,"href":"https:\/\/swarma.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=43425"}],"version-history":[{"count":0,"href":"https:\/\/swarma.org\/index.php?rest_route=\/wp\/v2\/posts\/43425\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/swarma.org\/index.php?rest_route=\/wp\/v2\/media\/43402"}],"wp:attachment":[{"href":"https:\/\/swarma.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=43425"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/swarma.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=43425"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/swarma.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=43425"},{"taxonomy":"special","embeddable":true,"href":"https:\/\/swarma.org\/index.php?rest_route=%2Fwp%2Fv2%2Fspecial&post=43425"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}