442 - Matrix Chain Multiplication

本文探讨了如何高效计算多个矩阵相乘所需的乘法次数,包括解析矩阵乘法原理、优化算法实现及处理输入格式的方法。

注: 此题比较无脑, 且没有意思, 建议不要做.
题意:

矩阵乘法, 要求计算出几个矩阵相乘时需要进行的相乘次数. 输入分为 first part 与 second part, first part 给出各个矩阵的行数与列数, second part 格式如下:
SecondPart = Line { Line } <EOF>
Line           = Expression <CR>
Expression  = Matrix | "(" Expression Expression ")"
Matrix        = "A" | "B" | "C" | ... | "X" | "Y" | "Z"
即每一行包含由括号括起来的表达式或矩阵, 按括号内的表达式先运算的规则进行矩阵乘法, 给出相乘的总次数.

思路:
1. 对于矩阵 A(m,n), 矩阵 B(n,p), 则 AB 的相乘次数为 m*n*p;
2. 题目是不需要考虑括号是否匹配的, 即 (AB)) 的结果不能是 error, 必须返回正常 AB 的结果, 所以按最无脑的写法, 碰到 字母 就进栈, 碰到 ) 就出栈两个数, 进行运算后入栈, 这样就可以 AC 了.
3. 使用 map 来存放每个矩阵的行与列.
4. 若 A 与 B 相乘, 则结果 AB 做为矩阵名, A 的行数做为 AB 的行, B 的列数做为 AB 的列, 并将结果存入 map 中.
5. 看到别人的代码直接使用一个结构体, 包含行与列, 即不考虑矩阵的名称, 直接把乘出来的结果生成一个新的结构体, 只包含行与列, 然后压栈, 这样可以不用使用 map, 也比较简单.
6. 本题有一个比较 SB 的地方就是, second part 输入有可能是空行, 此时, 必须不作处理. 一开始我写的代码, 会把空行的结果当 0 输出(即不做运算), 导致无限 WA; 题目这样太无聊了, 未说明的东西, 也放到 test case 里, 这不是拆腾人嘛.

要点:
char c 转 string: string str(1, c); 或 ""+c;

题目:
http://uva.onlinejudge.org/index.php?option=com_onlinejudge&Itemid=8&category=103&page=show_problem&problem=383

代码:

# include <iostream>
# include <string>
# include <cstdio>
# include <cstring>
# include <vector>
# include <algorithm>
# include <cctype>
# include <iterator>
# include <assert.h>
# include <map>
# include <stack>
using namespace std;

map< string, pair<int, int> > MATRIXS;

// 计算两个矩阵相乘需要的相乘元素个数, 若两个矩阵不能相乘, 则返回 -1
// 两矩阵可以相乘是指 left 的 column 要等于 right 的 row
// 两矩阵的总乘数是指 left.row * left.column * right.columnl
int getNumMulti(const string& left, const string& right) {
  if (MATRIXS.find(left) == MATRIXS.end() ||
      MATRIXS.find(right) == MATRIXS.end()) {
    return -1;
  }

  pair<int, int> matrixsLeft = MATRIXS[left];
  pair<int, int> matrixsRight = MATRIXS[right];

  if (matrixsLeft.second == matrixsRight.first) {
    return matrixsLeft.first * matrixsLeft.second * matrixsRight.second;
  } else {
    return -1;
  }
}

// 计算需要进行相乘的次数, 返回 -1 表示出错了
int getNumMulti(const string& line) {
  stack<string> stk;
  int numMulti = 0;

  for (int i=0; i<line.size(); i++) {
    if (isupper(line[i])) {
      string matrix(1, line[i]);
      stk.push(matrix);

    } else if (line[i] == ')') {
      string right = stk.top();
      stk.pop();
      string left = stk.top();
      stk.pop();

      int num = getNumMulti(left, right);
      if (num == -1) return -1;

      numMulti += num;
      string newMatrixs = left + right;
      stk.push(newMatrixs);
      MATRIXS[newMatrixs] = make_pair(MATRIXS[left].first, 
                                      MATRIXS[right].second);
    }
  }

  return numMulti;
}


int main(int argc, char const *argv[])
{
  #ifndef ONLINE_JUDGE
    freopen("442_i.txt", "r", stdin);  
    freopen("uva_o.txt", "w", stdout); 
  #endif
  
  int numMatrix;
  cin >> numMatrix;

  // 读入 first part
  string matrixName;
  int row, column;
  while (numMatrix--) {
    cin >> matrixName >> row >> column;

    MATRIXS[matrixName] = make_pair(row, column);
  }

  // 读入 second part
  string line;
  cin.ignore();         // cin 之后接 getline 要有 ignore
  while (!cin.eof()) {
    getline(cin, line);
    // 这里有可能会出现空行, 空行不做处理, 否则走到流程里, 会输出 0, 导致无限 WA 
    // 这种请况既然没有说明, 就不该出现在 test case 里
    if (line.size() == 0) continue;   

    int numMulti = -1;
    numMulti = getNumMulti(line);

    if (numMulti == -1) {
      cout << "error" << endl;
    } else {
      cout << numMulti << endl;
    }
  }
  
  return 0;
}

环境: C++ 4.5.3 - GNU C++ Compiler with options: -lm -lcrypt -O2 -pipe -DONLINE_JUDGE

==============================================================

附: 考虑比较完善的代码, 即当括号不匹配时会返回 error; 括号内为空时, 也会返回 error; 奈何题目根本不要求这么多.

# include <iostream>
# include <string>
# include <cstdio>
# include <cstring>
# include <vector>
# include <algorithm>
# include <cctype>
# include <iterator>
# include <assert.h>
# include <map>
# include <stack>
using namespace std;

map< string, pair<int, int> > MATRIXS;
// 算法: 碰到 非) 就进栈, 碰到 ) 就看栈顶是不是匹配的 (, 是就出栈;
// 连续碰到两个 矩阵 名, 就相乘, 把结果入栈
// 若只碰到一个矩阵, 则入栈

// 计算两个矩阵相乘需要的相乘元素个数, 若两个矩阵不能相乘, 则返回 -1
// 两矩阵可以相乘是指 left 的 column 要等于 right 的 row
// 两矩阵的总乘数是指 left.row * left.column * right.columnl
int getNumMulti(const string left, const string right) {
  if (MATRIXS.find(left) == MATRIXS.end() ||
      MATRIXS.find(right) == MATRIXS.end()) {
    return -1;
  }

  pair<int, int> matrixsLeft = MATRIXS[left];
  pair<int, int> matrixsRight = MATRIXS[right];

  if (matrixsLeft.second == matrixsRight.first) {
    return matrixsLeft.first * matrixsLeft.second * matrixsRight.second;
  } else {
    return -1;
  }
}

// 计算需要进行相乘的次数, 返回 -1 表示出错了
int getNumMulti(const string& line) {
  // 只有一位, 用这个是一个有效矩阵, 则返回 0
  if (line.size() == 1) {
    string matrix(1, line[0]);
    if (MATRIXS.find(matrix) != MATRIXS.end()) {
      return 0;
    } else {
      return -1;
    }
  }

  int numMulti = 0;
  stack<string> stk;

  for (int i=0; i<line.size(); i++) {
    // 没有碰到 ), 则直接入栈; 若碰到了, 则取栈顶两个元素进行操作
    if (line[i] != ')') {
      string matrix(1, line[i]);
      stk.push(matrix);

    } else {
      // 如果出现 () 或 A), 则返回 false
      if (stk.size() < 2) return -1;

      while (stk.top() != "(") {
        string right = stk.top();
        stk.pop();
        string left = stk.top();
        stk.pop();
        
        if (left == "(") {
          numMulti += 0;
          stk.push(right);
          break;
        } else {
          int num = getNumMulti(left, right);
          if (num == -1) return -1;

          numMulti += num;
          string newMatrixs = left + right;
          stk.push(newMatrixs);
          MATRIXS[newMatrixs] = make_pair(MATRIXS[left].first, 
                                          MATRIXS[right].second);
        }
      }
    }
  }

  if (!stk.empty()) {
    // 要考虑 AB 这样不带括号的情况
    while (stk.top() != "(") {
      if (stk.size() == 1) {
        stk.pop();
        break;
      } 

      string right = stk.top();
      stk.pop();
      string left = stk.top();
      stk.pop();
      
      if (left == "(") {
        return -1;
      } else {
        int num = getNumMulti(left, right);
        if (num == -1) return -1;

        numMulti += num;
        string newMatrixs = left + right;
        stk.push(newMatrixs);
        MATRIXS[newMatrixs] = make_pair(MATRIXS[left].first, 
                                        MATRIXS[right].second);
      }
    }
  }

  return numMulti;
}


int main(int argc, char const *argv[])
{
  #ifndef ONLINE_JUDGE
    freopen("442_i.txt", "r", stdin);  
    freopen("uva_o.txt", "w", stdout); 
  #endif
  
  int numMatrix;
  cin >> numMatrix;

  // 读入 first part
  string matrixName;
  int row, column;
  while (numMatrix--) {
    cin >> matrixName >> row >> column;

    MATRIXS[matrixName] = make_pair(row, column);
  }

  // 读入 second part
  string line;
  cin.ignore();       // cin 之后接 getline 要有 ignore
  while (!cin.eof()) {
    getline(cin, line);
    // 这里有可能会出现空行, 空行不做处理, 否则走到流程里, 会输出 0, 题目太SB了, 
    // 这种请况既然没有说明, 就不该出现在 test case 里
    if (line.size() == 0) continue;   

    int numMulti = -1;
    numMulti = getNumMulti(line);

    if (numMulti == -1) {
      cout << "error" << endl;
    } else {
      cout << numMulti << endl;
    }
  }
  return 0;
}

转载于:https://my.oschina.net/zenglingfan/blog/148609

import cvxpy as cp import numpy as np import pandas as pd data = pd.read_excel('附件1.xlsx') X = data.iloc[1:, 1:2] Y = data.iloc[1:, 2:3] W = data.iloc[2:, 3:4] # 收集点垃圾量(i=1..n) xi = X.values.flatten() # 转换为一维数组 (n,) yi = Y.values.flatten() # 转换为一维数组 (n,) wi = W.values.flatten() # 转换为一维数组 (n,) # 定义参数 n = 30 # 收集点数量(i=1..n,0为处理厂) K = 30 # 车辆总数 Q = 5 # 最大载重 # 计算距离矩阵dij(i,j=0..n) dij = np.zeros((n+1, n+1)) for i in range(n+1): for j in range(n+1): dij[i, j] = np.sqrt((xi[i] - xi[j])**2 + (yi[i] - yi[j])**2) x = cp.Variable((n+1, n+1, K), boolean=True) # x_ijv: 车辆v从i到j y = cp.Variable((n, K), boolean=True) # y_iv: 车辆v服务收集点i(i=1..n,索引0..n-1) z = cp.Variable(K, boolean=True) # z_v: 使用车辆v u = cp.Variable((n+1, K), nonneg=True) # u_iv: 车辆v访问i后的累计载重(i=0..n,u[0,v]=0) objective = cp.Minimize(cp.sum(cp.sum(cp.sum(dij @ x, axis=2), axis=1), axis=0)) # 约束条件 constraints = [] # 1. 每个收集点被 exactly 一辆车服务(i=1..n,对应y的索引0..n-1) for i in range(n): constraints.append(cp.sum(y[i, :]) == 1) # 2. 流量守恒(i=1..n,处理厂i=0的约束在6中) for i in range(1, n+1): # 收集点i(1..n,y索引i-1) for v in range(K): constraints.append(cp.sum(x[i, :, v]) == y[i-1, v]) # 离开i的次数等于服务i的次数 constraints.append(cp.sum(x[:, i, v]) == y[i-1, v]) # 进入i的次数等于服务i的次数 # 3. 载重约束 for v in range(K): constraints.append(cp.sum(wi * y[:, v]) <= Q * z[v]) # 4. MTZ约束(子回路消除) for v in range(K): constraints.append(u[0, v] == 0) # 处理厂出发时载重为0 for i in range(1, n+1): # 收集点i(1..n,wi[i-1]) constraints.append(u[i, v] >= wi[i-1]) # 载重下限 constraints.append(u[i, v] <= Q) # 载重上限 for j in range(1, n+1): if i != j: constraints.append(u[i, v] - u[j, v] + Q * x[i, j, v] <= Q - wi[j-1]) # 5. 车辆使用约束:服务收集点则使用车辆 for i in range(n): for v in range(K): constraints.append(y[i, v] <= z[v]) # 6. 出发返回约束(处理厂i=0,出发到收集点1..n,返回从收集点1..n到0) for v in range(K): constraints.append(cp.sum(x[0, 1:, v]) == z[v]) # 出发次数等于车辆使用 constraints.append(cp.sum(x[1:, 0, v]) == z[v]) # 返回次数等于车辆使用 # 建立问题并求解 problem = cp.Problem(objective, constraints) problem.solve() # 输出结果(示例,需根据实际需求处理) print("最优目标值:", problem.value) print("使用的车辆:", z.value)D:\python\python.exe D:\35433\Documents\数模练习\电工杯\问题1.py D:\python\Lib\site-packages\cvxpy\expressions\expression.py:674: UserWarning: This use of ``*`` has resulted in matrix multiplication. Using ``*`` for matrix multiplication has been deprecated since CVXPY 1.1. Use ``*`` for matrix-scalar and vector-scalar multiplication. Use ``@`` for matrix-matrix and matrix-vector multiplication. Use ``multiply`` for elementwise multiplication. This code path has been hit 1 times so far. D:\python\Lib\site-packages\cvxpy\expressions\expression.py:674: UserWarning: This use of ``*`` has resulted in matrix multiplication. Using ``*`` for matrix multiplication has been deprecated since CVXPY 1.1. Use ``*`` for matrix-scalar and vector-scalar multiplication. Use ``@`` for matrix-matrix and matrix-vector multiplication. Use ``multiply`` for elementwise multiplication. This code path has been hit 2 times so far. D:\python\Lib\site-packages\cvxpy\expressions\expression.py:674: UserWarning: This use of ``*`` has resulted in matrix multiplication. Using ``*`` for matrix multiplication has been deprecated since CVXPY 1.1. Use ``*`` for matrix-scalar and vector-scalar multiplication. Use ``@`` for matrix-matrix and matrix-vector multiplication. Use ``multiply`` for elementwise multiplication. This code path has been hit 3 times so far. D:\python\Lib\site-packages\cvxpy\expressions\expression.py:674: UserWarning: This use of ``*`` has resulted in matrix multiplication. Using ``*`` for matrix multiplication has been deprecated since CVXPY 1.1. Use ``*`` for matrix-scalar and vector-scalar multiplication. Use ``@`` for matrix-matrix and matrix-vector multiplication. Use ``multiply`` for elementwise multiplication. This code path has been hit 4 times so far. D:\python\Lib\site-packages\cvxpy\expressions\expression.py:674: UserWarning: This use of ``*`` has resulted in matrix multiplication. Using ``*`` for matrix multiplication has been deprecated since CVXPY 1.1. Use ``*`` for matrix-scalar and vector-scalar multiplication. Use ``@`` for matrix-matrix and matrix-vector multiplication. Use ``multiply`` for elementwise multiplication. This code path has been hit 5 times so far. D:\python\Lib\site-packages\cvxpy\expressions\expression.py:674: UserWarning: This use of ``*`` has resulted in matrix multiplication. Using ``*`` for matrix multiplication has been deprecated since CVXPY 1.1. Use ``*`` for matrix-scalar and vector-scalar multiplication. Use ``@`` for matrix-matrix and matrix-vector multiplication. Use ``multiply`` for elementwise multiplication. This code path has been hit 6 times so far. D:\python\Lib\site-packages\cvxpy\expressions\expression.py:674: UserWarning: This use of ``*`` has resulted in matrix multiplication. Using ``*`` for matrix multiplication has been deprecated since CVXPY 1.1. Use ``*`` for matrix-scalar and vector-scalar multiplication. Use ``@`` for matrix-matrix and matrix-vector multiplication. Use ``multiply`` for elementwise multiplication. This code path has been hit 7 times so far. D:\python\Lib\site-packages\cvxpy\expressions\expression.py:674: UserWarning: This use of ``*`` has resulted in matrix multiplication. Using ``*`` for matrix multiplication has been deprecated since CVXPY 1.1. Use ``*`` for matrix-scalar and vector-scalar multiplication. Use ``@`` for matrix-matrix and matrix-vector multiplication. Use ``multiply`` for elementwise multiplication. This code path has been hit 8 times so far. D:\python\Lib\site-packages\cvxpy\expressions\expression.py:674: UserWarning: This use of ``*`` has resulted in matrix multiplication. Using ``*`` for matrix multiplication has been deprecated since CVXPY 1.1. Use ``*`` for matrix-scalar and vector-scalar multiplication. Use ``@`` for matrix-matrix and matrix-vector multiplication. Use ``multiply`` for elementwise multiplication. This code path has been hit 9 times so far. D:\python\Lib\site-packages\cvxpy\expressions\expression.py:674: UserWarning: This use of ``*`` has resulted in matrix multiplication. Using ``*`` for matrix multiplication has been deprecated since CVXPY 1.1. Use ``*`` for matrix-scalar and vector-scalar multiplication. Use ``@`` for matrix-matrix and matrix-vector multiplication. Use ``multiply`` for elementwise multiplication. This code path has been hit 10 times so far. D:\python\Lib\site-packages\cvxpy\expressions\expression.py:674: UserWarning: This use of ``*`` has resulted in matrix multiplication. Using ``*`` for matrix multiplication has been deprecated since CVXPY 1.1. Use ``*`` for matrix-scalar and vector-scalar multiplication. Use ``@`` for matrix-matrix and matrix-vector multiplication. Use ``multiply`` for elementwise multiplication. This code path has been hit 11 times so far. D:\python\Lib\site-packages\cvxpy\expressions\expression.py:674: UserWarning: This use of ``*`` has resulted in matrix multiplication. Using ``*`` for matrix multiplication has been deprecated since CVXPY 1.1. Use ``*`` for matrix-scalar and vector-scalar multiplication. Use ``@`` for matrix-matrix and matrix-vector multiplication. Use ``multiply`` for elementwise multiplication. This code path has been hit 12 times so far. D:\python\Lib\site-packages\cvxpy\expressions\expression.py:674: UserWarning: This use of ``*`` has resulted in matrix multiplication. Using ``*`` for matrix multiplication has been deprecated since CVXPY 1.1. Use ``*`` for matrix-scalar and vector-scalar multiplication. Use ``@`` for matrix-matrix and matrix-vector multiplication. Use ``multiply`` for elementwise multiplication. This code path has been hit 13 times so far. D:\python\Lib\site-packages\cvxpy\expressions\expression.py:674: UserWarning: This use of ``*`` has resulted in matrix multiplication. Using ``*`` for matrix multiplication has been deprecated since CVXPY 1.1. Use ``*`` for matrix-scalar and vector-scalar multiplication. Use ``@`` for matrix-matrix and matrix-vector multiplication. Use ``multiply`` for elementwise multiplication. This code path has been hit 14 times so far. D:\python\Lib\site-packages\cvxpy\expressions\expression.py:674: UserWarning: This use of ``*`` has resulted in matrix multiplication. Using ``*`` for matrix multiplication has been deprecated since CVXPY 1.1. Use ``*`` for matrix-scalar and vector-scalar multiplication. Use ``@`` for matrix-matrix and matrix-vector multiplication. Use ``multiply`` for elementwise multiplication. This code path has been hit 15 times so far. D:\python\Lib\site-packages\cvxpy\expressions\expression.py:674: UserWarning: This use of ``*`` has resulted in matrix multiplication. Using ``*`` for matrix multiplication has been deprecated since CVXPY 1.1. Use ``*`` for matrix-scalar and vector-scalar multiplication. Use ``@`` for matrix-matrix and matrix-vector multiplication. Use ``multiply`` for elementwise multiplication. This code path has been hit 16 times so far. D:\python\Lib\site-packages\cvxpy\expressions\expression.py:674: UserWarning: This use of ``*`` has resulted in matrix multiplication. Using ``*`` for matrix multiplication has been deprecated since CVXPY 1.1. Use ``*`` for matrix-scalar and vector-scalar multiplication. Use ``@`` for matrix-matrix and matrix-vector multiplication. Use ``multiply`` for elementwise multiplication. This code path has been hit 17 times so far. D:\python\Lib\site-packages\cvxpy\expressions\expression.py:674: UserWarning: This use of ``*`` has resulted in matrix multiplication. Using ``*`` for matrix multiplication has been deprecated since CVXPY 1.1. Use ``*`` for matrix-scalar and vector-scalar multiplication. Use ``@`` for matrix-matrix and matrix-vector multiplication. Use ``multiply`` for elementwise multiplication. This code path has been hit 18 times so far. D:\python\Lib\site-packages\cvxpy\expressions\expression.py:674: UserWarning: This use of ``*`` has resulted in matrix multiplication. Using ``*`` for matrix multiplication has been deprecated since CVXPY 1.1. Use ``*`` for matrix-scalar and vector-scalar multiplication. Use ``@`` for matrix-matrix and matrix-vector multiplication. Use ``multiply`` for elementwise multiplication. This code path has been hit 19 times so far. D:\python\Lib\site-packages\cvxpy\expressions\expression.py:674: UserWarning: This use of ``*`` has resulted in matrix multiplication. Using ``*`` for matrix multiplication has been deprecated since CVXPY 1.1. Use ``*`` for matrix-scalar and vector-scalar multiplication. Use ``@`` for matrix-matrix and matrix-vector multiplication. Use ``multiply`` for elementwise multiplication. This code path has been hit 20 times so far. D:\python\Lib\site-packages\cvxpy\expressions\expression.py:674: UserWarning: This use of ``*`` has resulted in matrix multiplication. Using ``*`` for matrix multiplication has been deprecated since CVXPY 1.1. Use ``*`` for matrix-scalar and vector-scalar multiplication. Use ``@`` for matrix-matrix and matrix-vector multiplication. Use ``multiply`` for elementwise multiplication. This code path has been hit 21 times so far. D:\python\Lib\site-packages\cvxpy\expressions\expression.py:674: UserWarning: This use of ``*`` has resulted in matrix multiplication. Using ``*`` for matrix multiplication has been deprecated since CVXPY 1.1. Use ``*`` for matrix-scalar and vector-scalar multiplication. Use ``@`` for matrix-matrix and matrix-vector multiplication. Use ``multiply`` for elementwise multiplication. This code path has been hit 22 times so far. D:\python\Lib\site-packages\cvxpy\expressions\expression.py:674: UserWarning: This use of ``*`` has resulted in matrix multiplication. Using ``*`` for matrix multiplication has been deprecated since CVXPY 1.1. Use ``*`` for matrix-scalar and vector-scalar multiplication. Use ``@`` for matrix-matrix and matrix-vector multiplication. Use ``multiply`` for elementwise multiplication. This code path has been hit 23 times so far. D:\python\Lib\site-packages\cvxpy\expressions\expression.py:674: UserWarning: This use of ``*`` has resulted in matrix multiplication. Using ``*`` for matrix multiplication has been deprecated since CVXPY 1.1. Use ``*`` for matrix-scalar and vector-scalar multiplication. Use ``@`` for matrix-matrix and matrix-vector multiplication. Use ``multiply`` for elementwise multiplication. This code path has been hit 24 times so far. D:\python\Lib\site-packages\cvxpy\expressions\expression.py:674: UserWarning: This use of ``*`` has resulted in matrix multiplication. Using ``*`` for matrix multiplication has been deprecated since CVXPY 1.1. Use ``*`` for matrix-scalar and vector-scalar multiplication. Use ``@`` for matrix-matrix and matrix-vector multiplication. Use ``multiply`` for elementwise multiplication. This code path has been hit 25 times so far. D:\python\Lib\site-packages\cvxpy\expressions\expression.py:674: UserWarning: This use of ``*`` has resulted in matrix multiplication. Using ``*`` for matrix multiplication has been deprecated since CVXPY 1.1. Use ``*`` for matrix-scalar and vector-scalar multiplication. Use ``@`` for matrix-matrix and matrix-vector multiplication. Use ``multiply`` for elementwise multiplication. This code path has been hit 26 times so far. D:\python\Lib\site-packages\cvxpy\expressions\expression.py:674: UserWarning: This use of ``*`` has resulted in matrix multiplication. Using ``*`` for matrix multiplication has been deprecated since CVXPY 1.1. Use ``*`` for matrix-scalar and vector-scalar multiplication. Use ``@`` for matrix-matrix and matrix-vector multiplication. Use ``multiply`` for elementwise multiplication. This code path has been hit 27 times so far. D:\python\Lib\site-packages\cvxpy\expressions\expression.py:674: UserWarning: This use of ``*`` has resulted in matrix multiplication. Using ``*`` for matrix multiplication has been deprecated since CVXPY 1.1. Use ``*`` for matrix-scalar and vector-scalar multiplication. Use ``@`` for matrix-matrix and matrix-vector multiplication. Use ``multiply`` for elementwise multiplication. This code path has been hit 28 times so far. D:\python\Lib\site-packages\cvxpy\expressions\expression.py:674: UserWarning: This use of ``*`` has resulted in matrix multiplication. Using ``*`` for matrix multiplication has been deprecated since CVXPY 1.1. Use ``*`` for matrix-scalar and vector-scalar multiplication. Use ``@`` for matrix-matrix and matrix-vector multiplication. Use ``multiply`` for elementwise multiplication. This code path has been hit 29 times so far. D:\python\Lib\site-packages\cvxpy\expressions\expression.py:674: UserWarning: This use of ``*`` has resulted in matrix multiplication. Using ``*`` for matrix multiplication has been deprecated since CVXPY 1.1. Use ``*`` for matrix-scalar and vector-scalar multiplication. Use ``@`` for matrix-matrix and matrix-vector multiplication. Use ``multiply`` for elementwise multiplication. This code path has been hit 30 times so far. D:\python\Lib\site-packages\cvxpy\reductions\solvers\solving_chain_utils.py:41: UserWarning: The problem has an expression with dimension greater than 2. Defaulting to the SCIPY backend for canonicalization.
05-25
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