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#ifndef TIFALIBS_OPT_KNAPSACK_01 | ||
#define TIFALIBS_OPT_KNAPSACK_01 | ||
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#include "../util/traits.hpp" | ||
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namespace tifa_libs::opt { | ||
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//@param a a[i]={value, weight} | ||
//@param W max weight | ||
//@return max total value while total weight <= W | ||
template <int_c T> | ||
CEXP T knapsack_01(vecpt<T> CR a, T W) NE { | ||
const T vs = [k = std::views::keys(a)] { return std::reduce(k.begin(), k.end(), T{}); }(); | ||
const T _1 = W, _2 = vs, | ||
_3 = T(a.size() >= 128 ? -1_u64 : 1_u64 << ((a.size() + 1) / 2)), | ||
_min = min({_1, _2, _3}); | ||
assert(0_isz <= (isz)_min && (usz)_min * a.size() < (usz)1e9); | ||
if (_min == _1) { | ||
vec<T> dp(W + 1); | ||
for (auto [s, t] : a) | ||
for (u32 j = u32(W - t); (i32)j >= 0; --j) dp[j + t] = max(dp[j + t], dp[j] + s); | ||
return max(dp); | ||
} else if (const T inf = to_uint_t<T>(-1) / 2 - 1; _min == _2) { | ||
vec<T> dp(vs + 1, inf); | ||
dp[0] = 0; | ||
for (auto [s, t] : a) | ||
for (u32 j = u32(vs - s); (i32)j >= 0; --j) dp[j + s] = min(dp[j + s], dp[j] + t); | ||
T ans = 0; | ||
flt_ (T, i, 0, T(vs + 1)) | ||
if (dp[i] <= W) ans = max(ans, i); | ||
return ans; | ||
} else { | ||
auto f = [&](u32 l, u32 r) { | ||
vecpt<T> res(1 << (r - l)); | ||
res[0] = {0, 0}; | ||
flt_ (u32, i, 0, r - l) | ||
flt_ (u32, j, 0, 1 << i) { | ||
auto&& [v, w] = a[l + i]; | ||
res[je + j] = {res[j].first + w, res[j].second + v}; | ||
} | ||
sort(res); | ||
flt_ (u32, i, 1, (u32)res.size()) res[i].second = max(res[i].second, res[i - 1].second); | ||
return res; | ||
}; | ||
auto l = f(0, (u32)a.size() / 2), r = f((u32)a.size() / 2, (u32)a.size()); | ||
reverse(l), r.emplace_back(inf, inf); | ||
T ans = 0; | ||
u32 id = -1_u32; | ||
for (auto& [t, s] : l) { | ||
while (t + r[id + 1].first <= W) ++id; | ||
if (~id) ans = max(ans, s + r[id].second); | ||
} | ||
return ans; | ||
} | ||
} | ||
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} // namespace tifa_libs::opt | ||
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#endif |
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#ifndef TIFALIBS_OPT_KNAPSACK_MIXED_HUGE | ||
#define TIFALIBS_OPT_KNAPSACK_MIXED_HUGE | ||
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#include "../util/traits.hpp" | ||
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namespace tifa_libs::opt { | ||
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//@param a a[i]={value, weight, count} | ||
//@param W max weight | ||
//@return max total value while total weight <= W | ||
template <int_c T> | ||
CEXP T knapsack_mixed_huge(vec<pt3<T>> a, T W) NE { | ||
const u32 n = (u32)a.size(); | ||
assert(n < 500); | ||
const u32 lim = [k = max(n, 50_u32)] { return k * k * k + 1; }(); | ||
vec<T> dp(lim + 1, to_uint_t<T>(-1) / 2 - 1); | ||
dp[0] = 0; | ||
for (auto& [v, w, num] : a) { | ||
T have = min(num, (T)n); | ||
num -= have; | ||
for (T k = 1; k = min(have, k), have; have -= k, k <<= 1) | ||
for (u32 j = lim - 1; (T)j >= v * k; --j) dp[j] = min(dp[j], dp[j - v * k] + w * k); | ||
} | ||
vecu id(n); | ||
std::iota(id.begin(), id.end(), 0), sort(id, [&](auto x, auto y) { return a[x]._0 * a[y]._1 > a[y]._0 * a[x]._1; }); | ||
T ans = 0; | ||
flt_ (u32, j, 0, lim + 1) { | ||
if (W < dp[j]) continue; | ||
T rest = W - dp[j], now = j; | ||
for (auto i : id) { | ||
auto [v, w, num] = a[i]; | ||
T div = min(num, rest / w); | ||
rest -= div * w, now += div * v; | ||
} | ||
ans = max(ans, now); | ||
} | ||
return ans; | ||
} | ||
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} // namespace tifa_libs::opt | ||
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#endif |
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#ifndef TIFALIBS_OPT_LARSCH | ||
#define TIFALIBS_OPT_LARSCH | ||
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#include "../util/traits.hpp" | ||
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namespace tifa_libs::opt { | ||
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template <arithm_c T, class Func> | ||
requires requires(Func f, u32 x, u32 y) { {f(x,y)}->std::same_as<T>; } | ||
CEXP vec<T> larsch(u32 n, Func&& w, T inf = inf_v<T>) NE { | ||
vec<T> dp(n + 1, inf); | ||
vecu x(n + 1); | ||
auto chk = [&](u32 from, u32 to) { | ||
if (from >= to) return; | ||
if (T cost = w(from, to); dp[from] + cost < dp[to]) dp[to] = dp[from] + cost, x[to] = from; | ||
}; | ||
auto f = [&](auto&& f, u32 l, u32 r) -> void { | ||
if (l + 1 >= r) return; | ||
u32 m = l + (r - l) / 2; | ||
for (u32 i = x[l]; i <= x[r]; i++) chk(i, m); | ||
f(f, l, m); | ||
for (u32 i = l + 1; i <= m; i++) chk(i, r); | ||
f(f, m, r); | ||
}; | ||
dp[0] = 0, chk(0, n), f(f, 0, n); | ||
return dp; | ||
}; | ||
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} // namespace tifa_libs::opt | ||
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#endif |
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#ifndef TIFALIBS_OPT_LARSCH_2D | ||
#define TIFALIBS_OPT_LARSCH_2D | ||
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#include "../util/traits.hpp" | ||
#include "smawk.hpp" | ||
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namespace tifa_libs::opt { | ||
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template <arithm_c T, class Func> | ||
requires requires(Func f, u32 x, u32 y) { {f(x,y)}->std::same_as<T>; } | ||
CEXP vec<T> larsch_2d(u32 n, Func&& w, T inf = inf_v<T>) NE { | ||
using W = to_bigger_t<T>; | ||
vec<W> dp(n + 1, inf_v<W>); | ||
vec<T> ans(n + 1, inf); | ||
dp[0] = 0; | ||
auto f = [&](u32 j, u32 i) -> W { return i < j ? dp[i] + w(i, j) : inf_v<W>; }; | ||
flt_ (u32, d, 1, n) { | ||
auto argmin = smawk(n + 1, n + 1, [&](u32 i, u32 j, u32 k) { return f(i, j) <= f(i, k); }); | ||
for (u32 i = n; i >= d; --i) dp[i] = dp[argmin[i]] + w(argmin[i], i); | ||
ans[d] = (T)dp[n]; | ||
} | ||
return ans; | ||
}; | ||
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} // namespace tifa_libs::opt | ||
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#endif |
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#ifndef TIFALIBS_OPT_LEV_DIS | ||
#define TIFALIBS_OPT_LEV_DIS | ||
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#include "../util/util.hpp" | ||
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namespace tifa_libs::opt { | ||
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//@return ans[i][j] = lev(a[0..i), b[0..j)) | ||
template <common_range T> | ||
CEXP vvecu lev_dis(T CR a, T CR b) NE { | ||
const u32 n = (u32)a.size(), m = (u32)b.size(); | ||
vvecu ans(n + 1, vecu(m + 1)); | ||
flt_ (u32, i, 0, n + 1) ans[i][0] = i; | ||
flt_ (u32, i, 0, m + 1) ans[0][i] = i; | ||
flt_ (u32, i, 1, n + 1) | ||
flt_ (u32, j, 1, m + 1) ans[i][j] = min({ans[i - 1][j - 1] + !(a[i - 1] == b[j - 1]), ans[i][j - 1] + 1, ans[i - 1][j] + 1}); | ||
return ans; | ||
} | ||
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} // namespace tifa_libs::opt | ||
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#endif |
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--- | ||
title: knapsack_01 | ||
documentation_of: //src/code/opt/knapsack_01.hpp | ||
--- |
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--- | ||
title: knapsack_mixed_huge | ||
documentation_of: //src/code/opt/knapsack_mixed_huge.hpp | ||
--- |
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--- | ||
title: larsch | ||
documentation_of: //src/code/opt/larsch.hpp | ||
--- |
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--- | ||
title: larsch_2d | ||
documentation_of: //src/code/opt/larsch_2d.hpp | ||
--- |
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--- | ||
title: lev_dis | ||
documentation_of: //src/code/opt/lev_dis.hpp | ||
--- |
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\paragraph{复杂度} \(O\left(N\min\left\{W , \sum_i v_i , 2^{\frac{N}{2}}\right\}\right)\) |
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适用于物品个数和价值均较小 (\(\leq 50\)) 且总容量较大 (\(10^9\)) 的情况 |
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\fullref{sec:smawk-算法} 的变体, 大概适用于 1D1D 四边形不等式 | ||
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\paragraph{输入} Monge 函数 \(w(x,y)\), 范围 \([0,n]\times[0,n]\) | ||
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\paragraph{输出} \(a\), 其中 \(a_i=\texttt{dp}(i)\), \(\texttt{dp}(0)=0\), \(\texttt{dp}(i)=\min_{0\leq j<i}\{\texttt{dp}(j)+w(j,i)\},~(i>0)\) | ||
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易知 \(A\) 是 Monge 矩阵 | ||
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\paragraph{复杂度} \(O(n\log n)\) | ||
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\paragraph{参考链接} \qrcode{https://www.itcsc.cuhk.edu.hk/Winter_School/Winter_School_2010/Title_Abstract/PPT_Mordecai.pdf} \qrcode{https://www.cse.ust.hk/faculty/golin/Talks/Revisiting_Monge.pdf} \qrcode{https://noshi91.hatenablog.com/entry/2023/02/18/005856} | ||
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\paragraph{参考文献} \cite{larmore1991line} |
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大概适用于 2D1D 四边形不等式 | ||
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\paragraph{输入} Monge 函数 \(w(x,y)\), 范围 \([0,n]\times[0,n]\) | ||
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\paragraph{输出} \(a\), 其中 \(a_d=\texttt{dp}(n,d)\), \(\texttt{dp}(0,d)=0\), \(\texttt{dp}(i,d)=\min_{0\leq j<i}\{\texttt{dp}(j,d-1)+w(j,i)\},~(i>0)\) | ||
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\paragraph{参考链接} \qrcode{https://www.itcsc.cuhk.edu.hk/Winter_School/Winter_School_2010/Title_Abstract/PPT_Mordecai.pdf} \qrcode{https://www.cse.ust.hk/faculty/golin/Talks/Revisiting_Monge.pdf} | ||
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\paragraph{参考文献} \cite{larmore1991line} |
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对两个序列 \(a\), \(b\), 其 Levenshtein 距离 \(\operatorname{lev}(a,b)\) 定义为通过对 \(a\) 进行 (插入, 删除, 修改) 单个元素使其变为 \(b\) 的最少操作次数. 令 \(a'\) 表示 \(a\) 删除第一个元素后的序列, 则 | ||
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\[ | ||
\operatorname{lev}(a,b)=\begin{cases} | ||
\max\{|a|,|b|\}, & |a|=0 ~\text{or}~ |b|=0, \\ | ||
\operatorname{lev}(a',b'), & a_0=b_0, \\ | ||
1+\min\{\operatorname{lev}(a',b),\operatorname{lev}(a,b'),\operatorname{lev}(a',b')\}, & \text{otherwise}. | ||
\end{cases} | ||
\] |
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用于计算满足如下条件的矩阵 \(a\) 每行的 \(\operatorname{argmin}\): | ||
用于计算满足如下条件 (Monotone) 的矩阵 \(A\) 每行的 \(\operatorname{argmin}\): | ||
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\begin{center} | ||
令 \(h(r) = \operatorname{argmin}_i a_{r,i}\), 则 \(h\) 单调不降 | ||
令 \(h(r) = \max \operatorname{argmin}_i A_{r,i}\), 则 \(h\) 单调不降 | ||
\end{center} | ||
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不难发现可以分治: 对当前矩阵, 找到中间一行的 \(\operatorname{argmin}\), 然后就可根据这个将矩阵分为更小的两个矩阵 | ||
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如果 Monotone 矩阵 \(A\) 还满足任意 \(2\times 2\) 的子矩阵都是 Monotone 的, 则称 \(A\) 是 Totally monotone 的 | ||
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如果 Monotone 矩阵 \(A\) 还满足 \(\forall i,j,~A_{i,j}+A_{i+1,j+1}\leq A_{i+1,j}+A_{i,j+1}\), 则称 \(A\) 是 Monge 的 | ||
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显然若 \(A\) 是 Monge 的则一定是 Totally monotone 的 | ||
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\paragraph{复杂度} \(O(c(1+\log(r/c)))\), 其中 \(r\) 为矩阵行数, \(c\) 为矩阵列数 | ||
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\paragraph{参考文献} | ||
\paragraph{参考链接} \qrcode{https://www.itcsc.cuhk.edu.hk/Winter_School/Winter_School_2010/Title_Abstract/PPT_Mordecai.pdf} \qrcode{https://www.cse.ust.hk/faculty/golin/Talks/Revisiting_Monge.pdf} | ||
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\cite{enwiki:1033199110} | ||
\paragraph{参考文献} \cite{aggarwal1986geometric} \cite{enwiki:1033199110} |
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