diff --git a/cpp/convex.hpp b/cpp/convex.hpp
index 0839ce0..4352da5 100644
--- a/cpp/convex.hpp
+++ b/cpp/convex.hpp
@@ -1,25 +1,27 @@
 #pragma once
 #include <cassert>
-#include <vector>
 #include <functional>
+#include <limits>
 #include <stack>
 #include <type_traits>
 #include <optional>
+#include <vector>
 
 /**
- * @brief monotoneな行列において、各行の最小値を取る最小列番号を$O((H+W)\log H)$時間で得る
+ * @brief monotoneな行列において、各行の最小値と、それを取る最小列番号を$O((H+W)\log H)$時間で得る
  * @param h 行数(行番号は[0..h-1])
  * @param w 列数(列番号は[0..w-1])
  * @param f 行列の要素を与える関数(行番号と列番号を引数にとって値を返す)
  * @param comp 要素の比較関数(最小値を知りたい場合はデフォルトのstd::less<T>)
- * @return std::vector<int> 各行の最小値列番号
+ * @return std::pair<std::vector<T>, std::vector<int>> (各行の最小値, 各行の最小値列番号)
  */
 template <typename F, typename Comp = std::less<std::invoke_result_t<F, int, int>>>
-std::vector<int> monotone_minima(int h, int w, const F& f, const Comp& comp = Comp()) {
+std::pair<std::vector<std::invoke_result_t<F, int, int>>, std::vector<int>> monotone_minima(int h, int w, const F& f, const Comp& comp = Comp()) {
     using T = std::invoke_result_t<F, int, int>;
     assert(h >= 0);
     assert(w >= 0);
-    std::vector<int> res(h);
+    std::vector<int> res_value(h);
+    std::vector<int> res_idx(h);
     std::stack<std::tuple<int, int, int, int>> stk; // {i, j, l, r} : [i..j]行目の答えを求める。結果は[l..r]の範囲に収まることが保証されている。
     stk.emplace(0, h-1, 0, w-1);
     while(!stk.empty()) {
@@ -34,11 +36,12 @@ std::vector<int> monotone_minima(int h, int w, const F& f, const Comp& comp = Co
                 min_idx = k;
             }
         }
-        res[m] = min_idx;
+        res_value[m] = min_value;
+        res_idx[m] = min_idx;
         if(i <= m-1) stk.emplace(i, m-1, l, min_idx);
         if(m+1 <= j) stk.emplace(m+1, j, min_idx, r);
     }
-    return res;
+    return {res_value, res_idx};
 }
 
 /**
@@ -60,11 +63,10 @@ std::vector<std::invoke_result_t<F, int, int>> online_offline_dp(int n, const F&
         auto submat = [&](int i, int j) {
             return dp[l+j].value() + f(l+j, mid+i);
         };
-        std::vector<int> min_idx = monotone_minima(r-mid, mid-l, submat, comp);
+        std::vector<T> min_val = monotone_minima(r-mid, mid-l, submat, comp).first;
         for(int i = 0; i < r-mid; i++) {
-            T val = submat(i, min_idx[i]);
-            if(!dp[mid+i] || comp(val, dp[mid+i].value())) {
-                dp[mid+i] = val;
+            if(!dp[mid+i] || comp(min_val[i], dp[mid+i].value())) {
+                dp[mid+i] = min_val[i];
             }
         }
         if(r-mid >= 2) self(self, mid, r);
@@ -74,3 +76,46 @@ std::vector<std::invoke_result_t<F, int, int>> online_offline_dp(int n, const F&
     for(int i = 0; i <= n; i++) res[i] = dp[i].value();
     return res;
 }
+
+/**
+ * @brief aがconvex (a_{i+1}-a_i <= a_{i+2}-a_{i+1})のとき、c_k = min_{i+j=k}(a_i + b_j)なる長さN+M-1の配列を得る O((N+m)\log(N+M))
+ * @param a vector 配列1 (convexである必要があります)
+ * @param b vector 配列2
+ * @return c vector min-plus convolutionの結果
+ */
+template <typename T, std::enable_if_t<std::is_scalar_v<T>, std::nullptr_t> = nullptr>
+std::vector<T> min_plus_convolution_convex_arbitary(const std::vector<T>& a, const std::vector<T>& b) {
+    int n = a.size(), m = b.size();
+    for(int i = 0; i < n-2; i++) assert(a[i+1]-a[i] <= a[i+2]-a[i+1]);
+    auto mat = [&](int i, int j) {
+        int k = i-j;
+        if(k < 0) return std::numeric_limits<T>::max();
+        if(k >= n) return std::numeric_limits<T>::max();
+        return a[k] + b[j];
+    };
+    return monotone_minima(n+m-1, m, mat).first;
+}
+
+/**
+ * @brief aもbもconvex (a_{i+1}-a_i <= a_{i+2}-a_{i+1})のとき、c_k = min_{i+j=k}(a_i + b_j)なる長さN+M-1の配列を得る O(N+M)
+ * @param a vector 配列1 (convexである必要があります)
+ * @param b vector 配列2
+ * @return c vector min-plus convolutionの結果
+ */
+template <typename T, std::enable_if_t<std::is_scalar_v<T>, std::nullptr_t> = nullptr>
+std::vector<T> min_plus_convolution_convex_convex(const std::vector<T>& a, const std::vector<T>& b) {
+    int n = a.size(), m = b.size();
+    for(int i = 0; i < n-2; i++) assert(a[i+1]-a[i] <= a[i+2]-a[i+1]);
+    for(int i = 0; i < m-2; i++) assert(b[i+1]-b[i] <= b[i+2]-b[i+1]);
+    int i = 0, j = 0;
+    std::vector<T> c(n+m-1);
+    c[0] = a[i] + b[j];
+    for(int k = 1; k < n+m-1; k++) {
+        if(j == m-1 || (i < n-1 && a[i+1]+b[j] < a[i]+b[j+1])) {
+            c[k] = a[++i] + b[j];
+        } else {
+            c[k] = a[i] + b[++j];
+        }
+    }
+    return c;
+}
diff --git a/test/yosupo-min-plus-convolution-convex-and-arbitary.test.cpp b/test/yosupo-min-plus-convolution-convex-and-arbitary.test.cpp
new file mode 100644
index 0000000..45f4a29
--- /dev/null
+++ b/test/yosupo-min-plus-convolution-convex-and-arbitary.test.cpp
@@ -0,0 +1,14 @@
+#define PROBLEM "https://judge.yosupo.jp/problem/min_plus_convolution_convex_arbitrary"
+#include "../cpp/convex.hpp"
+#include <iostream>
+
+int main() {
+    int n, m; std::cin >> n >> m;
+    std::vector<int> a(n), b(m);
+    for(int i = 0; i < n; i++) std::cin >> a[i];
+    for(int i = 0; i < m; i++) std::cin >> b[i];
+    std::vector<int> c = min_plus_convolution_convex_arbitary(a, b);
+    for(int i = 0; i < n+m-1; i++) {
+        std::cout << c[i] << (i==n+m-2 ? '\n' : ' ');
+    }
+}
diff --git a/test/yosupo-min-plus-convolution-convex-and-convex.test.cpp b/test/yosupo-min-plus-convolution-convex-and-convex.test.cpp
new file mode 100644
index 0000000..18e60c3
--- /dev/null
+++ b/test/yosupo-min-plus-convolution-convex-and-convex.test.cpp
@@ -0,0 +1,14 @@
+#define PROBLEM "https://judge.yosupo.jp/problem/min_plus_convolution_convex_convex"
+#include "../cpp/convex.hpp"
+#include <iostream>
+
+int main() {
+    int n, m; std::cin >> n >> m;
+    std::vector<int> a(n), b(m);
+    for(int i = 0; i < n; i++) std::cin >> a[i];
+    for(int i = 0; i < m; i++) std::cin >> b[i];
+    std::vector<int> c = min_plus_convolution_convex_convex(a, b);
+    for(int i = 0; i < n+m-1; i++) {
+        std::cout << c[i] << (i==n+m-2 ? '\n' : ' ');
+    }
+}