JZOJ5771【NOIP2008模拟】遨游

Description

MWH寒假外出旅游，来到了S国。S国划分为N个省，第i个省有Ti座城市，编号分别为Ci1，Ci2，……CiTi（各省城市编号不会重复）。所有城市间有M条双向的道路连接，从任意一个城市出发，可到达一切城市，每条道路均须收费。
此时恰逢春运期间，S国交通运输局采取了优惠措施。当一条路的路费在[L..R]区间时，可免去。同时，每个省也有优惠措施，第i个省内的每条道路路费收其Xi%，连接第i个省和第j个省的每条道路路费收其(Xi%+Xj%)/2。
MWH想从城市s走到城市t，请求出一对L，R，确保：

MWH能免费到达目的地；
L≤R；
L、R均为整数；
L尽可能地大，R在满足L最大的前提下最小。

注意：因每条道路由各省的交通运输局直接管辖，所以每条道路的路费必须先得到省级优惠，再得到国家级优惠。

Input

第一行两个整数N，M。
接下来M行，每行三个整数，u、v、w，表示连接u、v的道路需收费w。
接下来N行，第i+M+1行有一个整数Ti，后面Ti个整数，分别是Ci1..CiTi（所有城市编号保证按正整数顺序给出1.. $\sum$ Ti）。
下一行N个整数X1..Xi。
最后一行，两个整数，s、t。

Output

一行两个整数，如题，L和R。

Sample Input

Sample Output

2 6

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" alt=" " />

Data Constraint

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M6qBvUQd2gjmPXDVcLAgAAKCJcAQAAKCJcAQAAKCJcAQAAKCJcAQAAKCJcAQAAKCJcAQAAKCJcAQAAKCJcAQAAKCJcAQAAKCJcAQAAKCJcAQAAKCJcAQAAKCJcAQAAKGoERuGdjcYptwUAAOAkSuLPwf4755PjOtlQTt1gX9QN6qBuUMexDx5xWhAAAEAR4QoAAEAR4QoAAEAR4QoAAEAR4QoAAEAR4QoAAEAR4QoAAEAR4QoAAEAR4QoAAEAR4QoAAEAR4QoAAEAR4QoAAEAR4QoAAECRUriaS7/RkEajb/4L2GVVL4mlT+Wgqgr9jT+WTqrG6JtulT/upPua1dIZi3/ujcPVOjhcRYXbk6nG1uAmzPs9+Rl5EgRBtHgjcaY9AhZ2qtTf2GDVGookamzmTqXX6MiYvemNckw5BJs+xy7fA2mee7NwtQ4LV/O+tIYLcWeBeCNHaZNw7bqTQL4HiW6tOZAX1/ycfnJ0AcUq9Te+jP8NZeGM5D1RY93JTFxZyMcX6QrA8R0WrrqT8BPApKu0NbhRvvz+mB9OW1rn3hRcrkr9jSfLhSmlp4fMUYmWtE0eW3x8cSoIwNExoB1n54//ydDsEN0XDtPjQP6v2Jx+f5etpKbc3Zsfi6WJXwBwXIQrnIEv485mYOnqVA9HQHEwbymLc28DLtBChi0ucMDpEK5wBk0ZfAepAe0/Pa7eAaCvOfhOD2QP7Pg7e4EDAQvHQ7jC+TUH8j1zzYfLobzR2wE4qq5MZuEVNPLK5aM4EsIVLkOrLfb6r59fOjscoLCOuGgCCXGdAMdCuMJliMfKbA9EBvbQfJCnvKsC/S/5yL2KEDeJ/gZHRrjCidnZtbOTOZrbelOz5xvJM4PacZCmDF6yp5jjua/ElZcBO9PbYvubzNgqO8ks/Q2OrBHYEX5FdzYaUnJ3OKlfo5c/V7Iz8tITReJm1KobdyYBlwveNNX+JtvW7Eg9ZuS+Srvqxs7qb69ITmL/hJ39zaHrPyhcATmoG9RB3aAO6gZ1HLtuOC0IAACgiHAFAACgiHAFAACgiHAFAACgiHAFAACgiHAFAACgiHAFAACgiHAFAACgiHAFAACgiHAFAACgiHAFAACgiHAFAACgiHAFAACgiHAFAACgqBEYhXc2GqfcFgAAgJMoiT8H+++cT47rZEM5dYN9UTeog7pBHcc+eMRpQQAAAEWEKwAAAEWEKwAAAEWEKwAAAEWEKwAAAEWEKwAAAEWEKwAAAEWEKwAAAEWEKwAAAEWEKwAAAEWEKwAAAEWEKwAAAEWEKwAAAEUHhitfxp1G+O3S66U/19kyXLG59BsV68YfSyfVti9bLau0wfWY99O1Y5at8tGsG+rrjztTf0Pd3LagxI67g5krgTPykjeEjxF3Vvo4XLe968YbBU5e3cS3J9vax4o4wfqmKm3wJ+yqGyt6byUo7WI064b6ungX2d9QNxevSn9z0Pp1n9wLRo4tIDcgXt2uOkUbdTzJuolryRkF6b5oFrjrTqtKG/wVO+sm/vBW/tlNs26or7/g8vob6uYvOHa4Uh5z1ZS7e9014hb48vtjfjhtaa1v82S5MDc9PZiqSmpJ23wkXHx8mUdVaYNrMf+cmv935bFb1kqzbqiv63Ts/oa6gfqA9ryiBcr5438yNJ2R+zLYdEb+r9hSur9rZlrHAX6xFK9Km+NuOk4m7lvcRynNVpp1Q31dpaP3N9QNRDtc+V/ykZvYgaT0hRAt09O5s0Amyb2mt5TFrtVUaYMrER0NEPktv4hGs26orytx4v6GuoGohitTwP+GsnBG8j4gWqFMUwbf4Xi/aPFG8tMzHV9nzOFylJt+iLwnamfmmtt60hlTOShCf4PTUwtX835LhgtHRu8DjlphP82BfNud5GIob1yrjDLui6Q+u3WfZcQ4FuyD/gYnoBKu5v2G9Kam35t9CwetUEurLWYfKT+/fv6/1xLj+qq0OfJm41SiwcC7mynWDfV1vY7Z31A3EIVw5Y87cbDKnMMG9hGPU1gPAm0+yFPeEYnkuL4qbU72AnBc8WDg6WdmIsZ4LNb9XfRea9YN9XW9jtnfUDewDpkHwhs5YRvm7UBSed3YuV6yE+lF879szQuzNa9RzjxqVdrgT9jV31SemFGzbqivi3eR/Q11c/F29jeHrr/+k8cFWrAQuG7XzqJdzeSfXIpmhsy23ZqYr2IbXLxKnd1qdu31UrCz0qwb6uuiXWx/Q91ctGOHq0b8JLnsZasldwO5qBvUQd2gDuoGdRy7bpQnEQUAALhthCsAAABFhCsAAABFhCsAAABFhCsAAABFhCsAAABFhCsAAABFhCsAAABFhCsAAABFhCsAAABFhCsAAABFhCsAAABFhCsAAABFhCsAAABFjcAovLPROOW2AAAAnERJ/DnYf+d8clwnG8qpG+yLukEd1A3qOPbBI04LAgAAKCJcAQAAKCJcAQAAKCJcAQAAKCJcAQAAKCJcAQAAKCJcAQAAKCJcAQAAKCJcAQAAKCJcAQAAKCJcAQAAKCJcAQAAKCJcAQAAKDowXM2l32iE3y69XvpznS0DgFyrfqcvhb2NP5ZOqm/KaVuljfa6ANyEg8LVvN+Tn5EnQRBEizcSZ9ojYAE4Cn/cMcGlJ9PyRtJpDUUSfdPMnUqv0ZGxv0cb7XUBuB1BiR1355q5Yh7nBrO9H4lrUadugJ11M3PDNq7pXLyRU9DPeMHIMX2QMzL/lXpw4JrHOiOvYhvtdeFY6G9Qx7HrRnnMlS+/P+aH05aW7ooB3LruJDwqNOmWNfJkuTBd0NODNFO3t6TtiCw+vkwvVaWN9roA3BLVcOWP/8nQdDTuyyDT0QDACfi/Yj/f3d9le6Cm3N2bH4uleFXaaK8LwE05MFz5Mu5sBnG2TLJyZ7s+WQLAkXhLWWi00V4XgJtyYLhqyuA7SA1o/+mZoNUZcygcAADcJN0xV82BfM9ckcVQ3rhgEAAA3CD9SURbbXHMj59fjl0BOLHC/idxsU2VNtrrAnBT9MNVPAZhe4AnABxZ80Ge8q7S87/kY3VVX5U22usCcFvqzwNh53FxgvQ0LtHcLttzvuCW7CgrINc+dVM8z1WQmg8rbh3NRZVsX6WN9rpwFPQ3qOPYdXPYJKJxp5JaXLqTW0dnhzpq9TfxsjVZZ7Zt3ge+Km201wV19Deo49h104ifJJedXqHkbiAXdYM6qBvUQd2gjmPXjf6YKwAAgBtGuAIAAFBEuAIAAFBEuAIAAFBEuAIAAFBEuAIAAFBEuAIAAFBEuAIAAFBEuAIAAFBEuMLJ+eNOODvu1tIZp7/8NmosnVS7vszrtAFwk87W39Av3TTCFc7EkZEXfrflZvkeSDPZxHZOraGYhus2M3cqvUZHxv4ebQDcuBP3N/RLKPviwR13A7l21Y03ckwbJ8h+126mVTBy8r4Adxa46y/qrdIGfwX9Deq4vP6GfukvOHZ/w5ErXChPlgvTJT49pD9dSkvajsji40v8Sm0AYBfN/oZ+CZwWxKXyf+XH/Li/a2buaMrdvfmxWIpXpc0pthXA36bZ39AvwfhvVwM7EA/Qt5BhqyHD9b9dmQUT6a7+6S1NC9NBla2iShv8KfQ3OI4T9jf0S5AK4So6NQlUt2sH2Rx8SzBI3jKXfqMnPfOwVIeHm0N/g33R3+AScVoQF6Ark5lrfk7llUtpABwV/Q2Oj3CFy9Bqi5Pz75/fbOfny68d0OC0pVWlzfG2GMBfdcz+hn4JQrjCpViNU1gNAm0+yFPelTX+l3ysrsSp0uZkLwDAn3HM/oZ+CdY554HAdSqvGzvXi2v+P8EbBaYv2p4XZuaG63LXjeP5Y5KPr9IGfwL9Deq4yP6GfuniHbu/IVxB3a66iSb1k9RSOLFe3Emtl62J+Sq2wcWjv0EdF9vf0C9dtGP3N434SXLZqzBK7gZyUTeog7pBHdQN6jh23TDmCgAAQBHhCgAAQBHhCgAAQBHhCgAAQBHhCgAAQBHhCgAAQBHhCgAAQBHhCgAAQJFeuPLH0mk0wom5+nO1tQJAzJdxJ+pj1ktRZ5Poj6KlL1stq7TRXheAm6AWruZvQ1k4TvqbxgFAybzfko8nL5xVOVxmrsi0tx2wbNBpDUVGm7Yzdyq9RkfG/h5ttNcF4HaUfTfOjrs3wu9QcoLRLPpCTJdvprxplesGSNi/bvK+DDe+bet73OwX+K6+U65KG+114Vjob1DHsetG4ciVL+PXqelb3mXQOnxtAFBNU+7us7d5slyYj3pPD+bepJa0zSe/xceX6bGqtNFeF4BbcnC48sf/ZLhw5WXQ3N0YANT48vtjfjhtWX+u83/F3nR/l+2P4iC2WIpXpY32ugDclMPClT+Wf8OFuLOJdJU2CAAq8b/kI3vUyFvKYtfjqrTRXheAm/LfIQ+OBrGP5J1kBeCkfBn/i/ofj6PmAC5M/XA170tv6sjIGwhdG4BTslcODhf0PwAuU+3Tgn442GEhw1ZibpfWMDxEPu0xzwuA45j3G+aDnYg7+5atg1atdjgdzM9vdhh5YnxWlTba6wJwU2qHq+bgezPfzGrxRmFH487svxmHBUCXP+7EwSqQSV4H03yQp7yr9JLjs6q00V4XgNuiOg+ExzxXYN4Z1LOrbryRE7bZOXdUOO9esh/KmQ+rShvtdeEo6G9Qx7Hr5qAB7QBwGnN5G0bX5S2GLWkM0/eawCXfq3OE3YkEM5FGryHTTQPxgsT4rCpttNcF4GY04gSXf2ejISV3A7moG9RB3aAO6gZ1HLtu9L64GQAAAIQrAAAATYQrAAAARYQrAAAARYQrAAAARYQrAAAARYQrAAAARYQrAAAARYQrAAAARYQrAAAARYQrAAAARYQrAAAARYQrAAAARYQrAAAARY3AKLyz0TjltgAAAJxESfw52H/nfHJcJxvKqRvsi7pBHdQN6jj2wSNOCwIAACgiXAEAACgiXAEAACgiXAEAACgiXAEAACgiXAEAACgiXAEAACgiXAEAACgiXAEAACgiXAEAACgiXAEAACgiXAEAACgiXAEAACg6KFz54074zdJbS2csvtYW4orNpR/WTN/8VwF/LJ1UfeW0rdJmn3a4SHv1N6euG2rrD7jA/oa6uV5BiR13B97IMW2cYOSVNsON2VU3VlQ7Ei9uMMtvFDjmfidRYDNX0jVXpc0+7XA2av3NqeuG2jqrP9vfUDdnVaVuDlr/IU9OuEKenUU7c8M27mxVQ3mdnReMHNPROKMgXV6zwF13SFXaVF0Xzk2nvzl13VBb5/Y3+xvq5tyOHa4Yc4XT605sVcukW9bIk+XC7EqfHqSZur0lbfNxb/HxJX6lNlXXhetw6rqhti7eRfY31M21I1zhMvm/8mN+3N81M3c05e7e/FgsxavSpuq6jvAScAanrhtq6zpQN1CmEK4WMmwxIA/KvKWpLIU2+7TDH7Cjvzl13VBb14G6gbKDwlVz8B0ebt0sM3FlKj0CFgBl9DcA/grl04Jdmcxc83Mqr2POGAM4JvobAJdJf8xVqy2O+kpxc+I6+vnN7jR9+bWDFZy2tKq0qbquI7wEnEC2vzl13VBb14G6gTL9cBWfS94eqAfsofkgT3lXzfhf8rG6yqZKm6rrOv4rwjFk+5tT1w21dR2oG2irPw+EnY8jM19IPCna9twduCU7yiqleN6ZIDU/Tdw6mhsm2b5Km33a4WzU+ptT1w21dVZ/tr+hbs5qn7qptf5Dnjw96220MPkZdhZt3KnkLVv1k22bF9yrtNmnHc5Ctb85dd1QW2fzp/sb6uZsjh2uGvGT5LKXOpfcDeSiblAHdYM6qBvUcey6YRJRAAAARYQrAAAARYQrAAAARYQrAAAARYQrAAAARYQrAAAARYQrAAAARYQrAAAARYQrAAAARYQrAAAARYQrAAAARYQrAAAARYQrAAAARYQrAAAARY3AKLyz0TjltgAAAJxESfw52H/nfHJcJxvKqRvsi7pBHdQN6jj2wSNOCwIAACgiXAEAACgiXAEAAIqqsUwAABhcSURBVCgiXAEAACgiXAEAACgiXAEAACgiXAEAACgiXAEAACgiXAEAACgiXAEAACgiXAEAACgiXAEAACgiXAEAACjSCVfzfvgN08mlP1dZMwBsbPU1HRn7Oe38sXRS7fqy1SVVaaO9LgA34eBwNe+bjqQ3FXcWSBBslklXY/MAIOKPO6av+ZGRt+lnZu5Chq1MwLJBpzUU0zDRbiq9ZBCr0kZ7XQBuR1Bix91BMHPDNiZYAWs76wbIUV43XjByJBBnZP4refMocMzjnJFX3i6YBe66XZU22uvCsdDfoI5j181BR67mn1Pz/648cpQKwDl4S1mYH/d3zdUNsjQ3OE8P0kw1bEnbpLDFx5f4ldporwvALTkgXPny+2N+uI9CtgJwXE0ZvLgmrQyl1RlHgcWejuuZD3jubDMMwf8V2y1twtbm8Xf35sdiKV6VNtrrAnBTDghX0Sc2kV8Zd9KD2RuMZgegrTuRwBuJYwOW7WdW45ySAzzjI1mlqrTRXheAm3L41YLTD5H3xGD2mfl0Oe1Jh5GcAJTN34apMLMYtuhrAFycw8OV+yKD5BHx7rOMGGsAQJm9Mrk3dTJXCxKwAFyeA8JVNGATAI7OH8vrVMQZvac+zHUnXvrDXKsttlv6+c2GrXiMqNOWVpU22usCcFMOCFfxgM3pZ2ayvHgs1v2dZId4AsBRNR/kKe/Iuf8lH6ur+qq00V4XgNty0DwQW3PM2KmvxDzOCZje5XbtrBsgR3ndxPNJiRukptWL59pLzSe1Nf9ezmOrtNFeF46C/gZ1HLtuDgtXVhywZL3Qodw6OjvUsbtuVqElveROYhwHnvWyNclnxTba64I6+hvUcey6acRPkstOq1ByN5CLukEd1A3qoG5Qx7HrRueLmwEAABAiXAEAACgiXAEAACgiXAEAACgiXAEAACgiXAEAACgiXAEAACgiXAEAACgiXOEMfBl3GuEkbuulPy9oOpZOsl2jL1stq7TZpx2AK3LB/Q190vU65/TwuE676sZ+/2Ted8FJ9ntMqnx3ZdXvt+R7MC8e/Q3q+LP9DX3SWR27vyFcQd3+dZP3RbfxbVvf0TYL3HWHVKVN1XXh3OhvUMff7G/ok87t2P0NpwVxAZpyd5+9zZPlwnyGe3ow9ya1pG0+7i0+vsSv1KbqugDchkvob+iTrh3hChfAl98f88Npm65lddOv2Jvu75qZtnHHuFiKV6VN1XVpvhwAF+wC+hv6pKtHuML5+V/ykf0U5y1lsetxVdrs0w7A9buE/oY+6er9t6uBvYIBOB5fxv+GsnBG4g2yn+Jwa+hvcFz0NziNneEqGvcFVLfPDnLeb8lw4cjIGwhdHehvsC/6G1wiTgvibOb9hvSmIu7sW7Y+RLba4pgfP7/ZYZ2J8RJV2lRdl85LAnChLqq/oU+6eoQrnIU/7sQdXSCTbk6D5oM85V01kxwvUaVN1XWpv0IAl+Li+hv6pOt3znkgcJ121Y03csI2O+dyiSf728z1lzM/TZU2+7TD2dDfoI4/29/QJ53VsfsbwhXUlddNNEmeFCxbHeBqNuXVsjXpXsU2+7TDWdDfoI4/3d/QJ53NsfubRvwkuexAwZK7gVzUDeqgblAHdYM6jl03jLkCAABQRLgCAABQRLgCAABQRLgCAABQRLgCAABQRLgCAABQRLgCAABQRLgCAABQRLgCAABQdEC4mku/0QhnOc1d+nO9rQSAtVXf05fCXsYfSyfVJ+W0rdLmHOsC8OcdEK66Mom+mzC9eCNxzL1Ou6W2kQBg+eOOCSQ9mZY3kk5rKDLy1v3SzJ1Kr9GRsb9Hm3OsC8B1KPviwR1355q5fKv3ratTN8DOuom/5NY1nYs3cgr6GS8YOXlfgBt9gW/0Rb1V2pxjXaiD/gZ1HLtulMdczeXTfqR0H6Wru2IAt647CY/2TEo7F0+WCxHn6UGaqdtb0nZEFh9f4ldqc451AbgWquHKH7/KVBwZPROtAJyB/ys/5sf9XTNzR1Pu7s2PxVK8Km3Osa4aLxfAZVIMV758fYQfzeQh238AwCl4S1lotDnHugBcjf/U1jR/k6HpPdzZQMhWAADgVikdufJl/BoOtpJHzggCAIAbphOu/C8JzwiOnhnIDuB8Wu1wKpif3+zwcF9+7aAnpy2tKm3Osa4aLxfAZVIJV/O3oSxM1/HEYCsA59R8kKe8q+9WHwDt1XpV2pxjXZq/BwDndfg8ENE8LeIysxUi1eoGSNunbornuQpS82HFraM5ppLtq7Q5x7qwN/ob1HHsujk4XEWdXLLDwK2js0MdO+smDid5y9YknNm2W5N3VmxzjnVhL/Q3qOPYddOInySX/e6rkruBXNQN6qBuUAd1gzqOXTfKM7QDAADcNsIVAACAIsIVAACAIsIVAACAIsIVAACAIsIVAACAIsIVAACAIsIVAACAIsIVAACAIsIVAACAIsIVAACAIsIVAACAIsIVAACAokZQ8rXQ9lujAQAArk1J/DnYf+d8clwnG8qpG+yLukEd1A3qOPbBI04LAgAAKCJcAQAAKCJcAQAAKCJcAQAAKCJcAQAAKCJcAQAAKCJcAQAAKCJcAQAAKCJcAQAAKCJcAQAAKCJcAQAAKCJcAQAAKCJcAQAAKCJcAQAAKDo8XM370mg0EktHxr7ClgEAAPxBB4Urf9yRRu9HRl4gQRAtM3chwxYBCwAA3KYDwpUvXx8LEedJHpqbW7vPI3FkIR9fpCsAAHB79MdceUsTrUTu75o7mwIAAFybA8JVUwYvrshiKK3OWMLjVP5YOr2piDuTSVdrEwEAAP6ORmAHShXd2WhIyd0RG6haw/BoleWMPPkecNTqllWqGyCDukEd1A3qOHbdHHxacP62CVbWYtiSDqPZAQDAjTooXM37DelNnczVggQsAABwu+qHK38sr1N7GvBdkmcBuxNPRo4JWB9fQrwCAAC3hhnaAQAAFNUPV80HebJHqIZvMk/ePn+TYTj91YMwrB0AANyaA68W9GXcaYVhKsmdBUzFcMO4egd1UDeog7pBHceum8OnYgAyqBvUQd2gDuoGdVz8VAwAAADYIFwBAAAoIlwBAAAoIlwBAAAoIlwBAAAoIlwBAAAoIlwBAAAoIlwBAAAoIlzh5PxxJ5zAbWvpjLe/7NsfSyfVrp/+uqWqbfZphws3l/6u9+/UdUMNXqyL7m+oh+sVlNhxN5BrV914I8e0cYKRt2NF3ihwzLqcRMOZK+nHVmmzTzucTZX+JqodiRc3mOU3Om3dUINn9Wf7G+rhrI6dbwhXUKfT2XnByDEdjTMK0s1mgbvukKq0qbounNvO/mbmhm3c2aqG8sLVqeuGGjy3v9nfUA/ndux8w2lBXChPlgvTJT49SDN1e0va5uPe4uNL/Eptqq4LF687Cb8LrPxL4U9dN9TgdbjUusFfRbjCZfJ/5cf8uL9rZu5oyt29+bFYilelTdV1HeEl4AxOXTfU4HW41LrBn0W4wpksZNgqGcjpLU2LHaq02acd/r5T1w01+EdcWH9DPVy9/3Y1sIUIaGoOviUYJG+xV3/1pGdKbRZMpPSsDwDsgf4G57DzyFUQDXpnYam87K8rk5lrfk7ldcxIAwDHRH+D4+O0IC5Dqy1Ozr9/frOdny+/drCC05ZWlTZV16X2QnBWp64bavBvOnd/Qz1cPcIVLkM8BmE9wLP5IE95V834X/KxusqmSpuq6zrqi8PJnLpuqMG/6dz9DfVw/YISO+4GcpXXjZ3HJTM/UTyZ3tacL4l5jeKG0dwwycdXabNPO5zNPv1N8TxXwenrhho8qz/b31APZ3XsfEO4grpddZOeZTtaCifNizug9bI16V7FNvu0w1ns7G+y719Z/Zy6bqjBs/nT/Q31cDbHzjeN+Ely2SsFS+4GclE3qIO6QR3UDeo4dt0w5goAAEAR4QoAAEAR4QoAAEAR4QoAAEAR4QoAAEAR4QoAAEAR4QoAAEAR4QoAAEDRweHKH3fCybjWS2csfM84AAC4VQeFq3m/Ia2hyMgLv0bHLDNxF0NpEbAAAMCNqv/1N/5YOlGyku9B4vu7531p9KbizgKZdLU3F38BX0eBOqgb1EHdoI6L//qb+7tm+obuo7jmx88vx64AAMDtOThcFYWoxdI7dNUAAAB/Tv1w1XyQJ8eEqOGbzJO3zz9levBmAQAA/E0HHLlqyuDbk5EzlV7yasFPCU8LAgAA3KIDTwvagLW6UjBeJo/hPU67pbB5AAAAf4v+JKLxacGtge4AAAA3QD1czT9NtHJG8sw0DAAA4Ab9p7kyO6loz2QrdzYQjlsBAIBbdMCRK1/GnUbqq296U1dmAZOHAgCA21V/hnagAHWDOqgb1EHdoI6Ln6EdAAAAG4QrAAAARYQrAAAARYQrAAAARYQrAAAARYQrAAAARYQrAAAARYQrAAAARYQrAAAARYQrAAAARYQrAAAARYQrAAAARYQrAAAARYQrAAAARY3AKLyz0TjltgAAAJxESfw52H/nemIAAIBrxGlBAAAARYQrAAAARYQrAAAARYQrAAAARYQrAAAARYQrAAAARYQrAAAARYQrAAAARYQrAAAARYQrAAAARYSrHP64I/25yoqk0+lIp9bK5tJv9CX1yHlfOmNfYcP244/Hsv0KzPZ1xrK1NfY1Nxr1f3/h4zty8pcZv1fjre2270PjLL93AMDfdB3hyoSORtne3N6fFwRy+fL1sZBpT2kHv7iXl0l3/8fNP2Vq/tfLvK7FsBUFF/uasuGr+sr3CgzNwZ18bgWmrkzeRf5lgtD8bSgLdyb5L9mXcacRfiF4etm8Dv/rwzz+RQbN/V/PVg2E77tZd+X38Ukesttt3wdnJO/7bRAA4IZdbriKj4Bs7TDN7eHRhdX9dun9yKj9aXb+vszDPWlmJ96b2lQirSqHU/wv+Vg4MvK+C3bwq+c4rvmn2WYTUoJsSjE7+md7U3ciQfAYhp78YFkUZOzSM7EtCmr592dDW1eeR44JnJnbmwN5cRfy8RX/Psx78io525x+AeZ3G4RfCh4uM9e8pGfpxttsg615os229Pu7g2AYRF2ZZZ43/B1KW1oH5KJwHbZ2Vttjwlq/U/Q7r6GozgEAf1dwUWaBazbJbpbZ4wZeXhNvFDiF93uByQDm8U4wyn1wzvOkFjcYjZyc23OWzPN7VR+3WtzZju1zg60WM3f7defdtlPB+nc8ZhT+UnN+d/b5V+9L6e/Jvj+Z98Zsv7O6wa4j7/Xt2FaTzzbrWD+V3R7zXLNR4JYXQ/Fzx6819Vat1rvfLzy7xbvrHADwZx0errZ2qvvvtN19Hhs+X1G7KFyV5pYK21L/8dnNKdvWsoc522HBqhWk7MPM+pxqoa/Wa88NJrkNS8OVfd2u6ya2ocr7mR8Uk4HLvv78MJQMOU66js3rmdntmZltcDevza63PBiXbWedv5HV4/IfUxjqs+9H1b/TKu0O/psHgOt2WLiKO9lkEAh3PnsdOdqzYz5iuAp3VGrJKqgZrja/G3dUcjRoawdX/Du370n6ZeUEksoBaXs785e87SkLV2Z94fPbNtG2VXk/coPoVgjNed6tl5N5jLc54rV+jgpH0TIrrV/nQTY4lYWrHa+t6t9plXa1/+YB4HYcEK7iU3AFp1LyjrxEnfChn3QLdurrHfMBR192HtnRDYJezs4o3JG7bn7QsTt3++L2DEKb3/t+pzrzfieb323xqcXCI2/r07ZRfay2y/63N3I3O+cwxFT5fSeC6M7tKj8qGYaU9etfBb2y5ynZKo06j38H0dvtHBCuqv6dVmm3/988ANyiAwa0e7JcmG796UHS44Vb0jZ75MXHV+LqvGhwtR1XHg1onkiN6+cSXJkFm0HRZgeT2o5pbzUwO7qSbd5P/3ub2b5/0VVuQWK9QfY51gOvNzbrzllaZp0yldfxOLqaLbW0pNXKDtSey9vHk7w/tw/67eT+xmbJ1zMzv8H079Dspc07k9EcyHdi4LkUXgW4j2hA+7v8k89Hu96ZvAw8+ZLnzQUE3WcZ2Y1x2qaaivnjV5FZ9Foew+2yVw1+ymOQrS9bfz2ZuuY19PIG7ccD6cOB6/b2rky+B4m6jgb07379inUeXrAQKPy+q/6dVmm3z988ANyu+uHK/5Uf8+P+LnspVlPu7s2PxdJ0xZvbBt+rHXl4/X7BVWk6NkEieq5wJx7+O/8KwHm/JR/b0aKS7iQ/jG3CiitP5v8fC0Lbd2KD5n0TDFI79TT/96fWNlqbwLm6WnBqcsbqCriOdGwQTL1nlda6WUdiaQ0XBe1tHWTeg25X7P8Gqd9DS5YvJtA+fYRX6eVfDDmWNxPR1uEjvOpuFazyppowYed5IhPzO5+5Nlgmgk94haiEV2J6XlteM3Vp5z1rfdyL+9PbcVXfaeu8kqp/p1Xa7fU3DwC3q3648pZStAstl9wBxVMJ7LMDijv4Yo60yw53ZNgjTzZ8vdsElJwCoHJgKFivne/JccKAdfcg8rlrTql532xH+ZEOb7kQZ58Xl1B65Orl3myrCRZbR3x2rjV99CtxlC9fFHpW+WSacxRp9X7Y0NQcfIch9SdzJDJczz+R51RKe5D31Pa78lIwN1V3kn6ddm6t+1EchsPpJX7k199sT2v5IsG3CWambmcS1cjumRMOrPO9LGTYKphKo+rfaZV2tf/mAeC2nHmeq254JGH/HVD6iMkm+ESnLaqJTuGsduShHacFKzNBqTc1wSNMbBLusB+X/8onJe1Odp5ysgeuto8a7GaPrpWu2zz36ijRfjv/fY9cWatTeMnAZ8JOPN+TPaWWOsoWnlp1xHVHJsitjnplT9sZzWbhEb9y9pTgvTw+bG7pTszzeP3w+cOjnolfXnikcuZunXouV7fOdwsDaJANzvZ9OePRMgC4cScKV8kJLYt2RgU7oLxDBOFRlpzgY49qOU/ysHMva4+g/BN51xjTkmFDgp3UNDPepvv8JB+tQ3Z4Njhugkml7SgaC5Y9LWiWf/Ie/y5/pFd5Nvt9j1zliWdXD98OEwwy7214etW8p8+TQfWjamEdlI/X2rT9EnlJvFfzKFQ1Ph8zISozKe06iCdPcyrXeS1m/XZ8XDjWjxFQAHAO9cNVqx2eRvn5zXbg0RGW9GDkorEoFXZA2fSTc2rCfnq3Y5fs6R3ZGmxbtP6c8VcHnxY0QaE1lPtZzrqbA3m3waXuEQU7C7n7WD1g2OfzvMKjcOnThHbsl3nn/PhISMm4r7Q6R66y4ve68nNqMmHoTdJfeRMPJF/X3dbRTE9Gjh2/lfdOKNb5IeK/zeR/7/w7rdJur795ALhd9cNV80Ge8q4QigcHb19RtH7gnjugNDuoO3/dc3kb2mx1wC76oNOC9qjFq7S94qNhNrjMXBtI9v3eQrPu16m4ZYetVl8LlHw+86tYfVWPHZS9Og3VHLxL+zUx4NukKt/8Rr23oq+ZiZ5fpp+ZYFh85GqxrDi0OTxSpPtFzZVDtu/J3fOxQt1hdX6Q+ANIeAq56t9plXa1/+YB4LYccFrQ7Dxe3PDy9bf1Hjee0qBkMPHWOvbcAZn9Rm6AspflT/f+wl8l4Sk4e16r6PsIN+yYnZmbGIBc5RTc/E2G9zWmQTCPe/3d/HMzlUT83q14X2K/HtBu28uyldgm32QfG8qiU6jRaazVe2OPuuQPgA+PfpVs7K+9+m64iE652dNvBVdx1mPHUFUM2c2udE9SL8cKWvaUauZIaHhaerr5DsrKf6dV2mn8zQPADag4H1ax9YSPmt+VtplsMj1Dd3Zyx123V59UdNds4EUTY4YTRpZ+D2LJ5I/FM1omHldxNvXVBKObG1KTZhZP7BlkZh5PPl/ezOab92b373Uz23pyIs7iySbLJogteor07zj/dVaYob3wew2rzj5fR1Gdlz13+veX9/U3hV+dVOV3WqXdUf7mAeB6XNgXN5dLzeK9uTWxA8+774BwlZmhPG+nnTfLevrxNWbpXj+u6LUldspFM8hnw1bJzjp3517B7pnf9wwheQFn12z0qd9VUXA7IFxt/V4qrAsAcNP+UzwIdlzhpJHPMsmceZj3o9NxhSeh7lenR3bbGtMUzlA+iMYFvbblfXuUejiuacdKa83SfT96lN9O0WuLTjMNylZgB2ZvDdSe1NiSsqewA7FVV2kHCmXG7dzJy3u3dCzP02wirXFfft8D+c5taK+0vJfHPc9aheP72s/7PQgAcPMaNmGdeyMAAACuxZknEQUAALguhCsAAABFhCsAAABFhCsAAABFhCsAAABFhCsAAABFhCsAAABFhCsAAABFhCsAAABFhCsAAABFhCsAAABFhCsAAABF/weGaHiYhtV3/gAAAABJRU5ErkJggg==" alt=" " />

想到正解了但是写萎了233.

emmmmm...二分两次，先二分l，跑spfa判断是否S与T联通。

然后再二分r，spfa...

其实不用spfa也行，spfa复杂度有点不好看。、

貌似并查集可以搞一搞，好像做过类似的题。

#include <iostream>

#include <cstdio>

#include <cstring>

#include <queue>

#include <cmath>

#include <algorithm>

using namespace std;

inline int read() {

    int res=;char c=getchar();bool f=;

    while(!isdigit(c)) {if(c=='-')f=;c=getchar();}

    while(isdigit(c))res=(res<<)+(res<<)+(c^),c=getchar();

    return f?-res:res;

}

#define N 50005

#define M 200005

int n, m, S, T;

struct edge {

    int nxt, to, from;

    double val;

}ed[M*];

int head[N], cnt;

inline void add(int x, int y, int z)

{

    ed[++cnt].from = x, ed[cnt].to = y;

    ed[cnt].nxt = head[x], ed[cnt].val = z;

    head[x] = cnt;

}

int belong[N];

int youh[N];

int Lans, Rans, l = 1e9, r;

double dis[N];

bool ex[N], can[M*];

int res;

int pre[N];

inline void spfa()

{

    for (int i =  ; i < N ; i ++) dis[i] = 1e9;

    memset(ex, , sizeof ex);

    dis[S] = ;

    queue <int> q;

    q.push(S);

    while(!q.empty())

    {

        int x = q.front();q.pop();

        ex[x] = ;

        for (register int i = head[x] ; i ; i = ed[i].nxt)

        {

            if (can[i]) continue;

            int to = ed[i].to;

            if (dis[to] > dis[x] + ed[i].val)

            {

                dis[to] = dis[x] + ed[i].val;

                if (!ex[to]) ex[to] = , q.push(to);

            }

        }

    }

}

inline bool check(int mid)

{

    memset(can, , sizeof can);

    for (register int i =  ; i <= cnt ; i ++) if (ed[i].val < (double)mid) can[i] = ;

    spfa();

    return dis[T] != 1e9;

}

inline bool Check(int mid)

{

    memset(can, , sizeof can);

    for (register int i =  ; i <= cnt ; i ++)

    {

        if (ed[i].val < (double)Lans) can[i] = ;

        if (ed[i].val > (double)mid) can[i] = ;

    }

    spfa();

    return dis[T] != 1e9;

}

int main()

{

//    freopen("trip.in", "r", stdin);

//    freopen("trip.out", "w", stdout);

    n = read(), m = read();

    for (register int i =  ; i <= m ; i ++)

    {

        int x = read(), y = read(), z = read();

        add(x, y, z), add(y, x, z);

    }

    for (register int i =  ; i <= n ; i ++)

    {

        int t = read();

        for (register int j =  ; j <= t ; j ++)

            belong[read()] = i;

    }

    for (register int i =  ; i <= n ; i ++) youh[i] = read();

    S = read(), T = read();

    for (register int i =  ; i <= cnt ; i ++)

    {

        int x = ed[i].from, y = ed[i].to;

        ed[i].val = (double)(((double)youh[belong[x]] + (double)youh[belong[y]]) / ) * ed[i].val;

        r = max(r, (int)ed[i].val + );

        l = min(l, max((int)ed[i].val - , ));

    }

    int ll = l, rr = r;

    int mid;

    while(l <= r)

    {

        mid = (l + r) >> ;

        if (check(mid)) l = mid + , Lans = mid;

        else r = mid - ;

    }

    ll = Lans;

    while(ll <= rr)

    {

        mid = (ll + rr) >> ;

        if (Check(mid)) rr = mid - , Rans = mid;

        else ll = mid + ;

    }

    printf("%d %d\n", Lans, Rans);

    return ;

}

巴特西