Matrix Chain Multiplication in DSA C++ - Time & Space Complexity

Time Complexity: Matrix Chain Multiplication

O(n³)

Understanding Time Complexity

We want to understand how the time needed to solve the Matrix Chain Multiplication problem grows as the number of matrices increases.

Specifically, how the number of calculations changes when we try to find the best way to multiply many matrices.

Scenario Under Consideration

Analyze the time complexity of the following code snippet.

int matrixChainOrder(int dims[], int n) {
    int dp[n][n];
    for (int i = 1; i < n; i++)
        dp[i][i] = 0;
    for (int length = 2; length <= n - 1; length++) {
        for (int i = 1; i <= n - length; i++) {
            int j = i + length - 1;
            dp[i][j] = INT_MAX;
            for (int k = i; k < j; k++) {
                int cost = dp[i][k] + dp[k+1][j] + dims[i-1]*dims[k]*dims[j];
                if (cost < dp[i][j]) dp[i][j] = cost;
            }
        }
    }
    return dp[1][n-1];
}
    

This code finds the minimum number of scalar multiplications needed to multiply a chain of matrices.

Identify Repeating Operations

Identify the loops, recursion, array traversals that repeat.

• Primary operation: The triple nested loops that check all ways to split the matrix chain.
• How many times: The outer two loops run roughly n² times, and the innermost loop runs up to n times for each pair, making about n³ operations.

How Execution Grows With Input

As the number of matrices (n) grows, the number of calculations grows much faster because we try many ways to split the chain.

Input Size (n)	Approx. Operations
10	About 1,000
100	About 1,000,000
1000	About 1,000,000,000

Pattern observation: The operations grow roughly by the cube of the input size, so doubling n makes the work about eight times bigger.

Final Time Complexity

Time Complexity: O(n³)

This means the time needed grows roughly with the cube of the number of matrices, so more matrices make the problem much harder.

Common Mistake

[X] Wrong: "Since we use dynamic programming, the time must be linear or quadratic."

[OK] Correct: The problem requires checking all pairs and splits, which leads to three nested loops, so the time is cubic, not just linear or quadratic.

Interview Connect

Understanding this time complexity helps you explain why some problems need more work even with smart methods, showing your grasp of algorithm efficiency.

Self-Check

"What if we used recursion without memoization? How would the time complexity change?"

Practice

1. What is the main goal of the Matrix Chain Multiplication problem?

easy

A. To find the maximum number of matrices that can be multiplied

B. To multiply all matrices in the chain directly without optimization

C. To find the minimum number of scalar multiplications needed to multiply a chain of matrices

D. To count the total number of matrices in the chain

Solution

1. Step 1: Understand the problem objective

   The Matrix Chain Multiplication problem aims to find the best way to multiply matrices to minimize computation.

2. Step 2: Identify the correct goal

   The goal is to minimize the number of scalar multiplications, not just multiply or count matrices.

3. Final Answer:

   To find the minimum number of scalar multiplications needed to multiply a chain of matrices -> Option C

4. Quick Check:

   Goal = Minimum multiplications [OK]

Hint: Remember: minimize multiplication cost, not just multiply [OK]

Common Mistakes:

• Thinking the goal is to multiply matrices directly without optimization
• Confusing counting matrices with minimizing operations
• Assuming maximum multiplications is the goal

2. Which of the following is the correct way to declare the dimensions array for 4 matrices with dimensions 10x20, 20x30, 30x40, and 40x50 in C++?

easy

A. int dims[] = {10, 20, 30, 40, 50};

B. int dims[] = {10, 20, 30, 40};

C. int dims[] = {10, 20, 30, 40, 50, 60};

D. int dims[] = {20, 30, 40, 50};

Solution

1. Step 1: Understand the dimensions array format

   For n matrices, the dimensions array has n+1 elements representing rows and columns of each matrix.

2. Step 2: Apply to given matrices

   For matrices 10x20, 20x30, 30x40, 40x50, dims = {10, 20, 30, 40, 50}.

3. Final Answer:

   int dims[] = {10, 20, 30, 40, 50}; -> Option A

4. Quick Check:

   Dimensions array length = number of matrices + 1 [OK]

Hint: Dimensions array length = matrices count + 1 [OK]

Common Mistakes:

• Using fewer elements than needed in the dimensions array
• Including extra unrelated numbers
• Confusing matrix dimensions order

3. What is the output of the following C++ code snippet for Matrix Chain Multiplication cost calculation?

int dims[] = {5, 10, 3, 12, 5};
int n = 4;
int m[5][5] = {0};
for (int L = 2; L <= n; L++) {
  for (int i = 1; i <= n - L + 1; i++) {
    int j = i + L - 1;
    m[i][j] = 1000000;
    for (int k = i; k < j; k++) {
      int cost = m[i][k] + m[k+1][j] + dims[i-1]*dims[k]*dims[j];
      if (cost < m[i][j]) m[i][j] = cost;
    }
  }
}
std::cout << m[1][n] << std::endl;

medium

A. 270

B. 150

C. 201

D. 405

Solution

1. Step 1: Understand matrix dimensions and indices

   Dimensions: 5x10, 10x3, 3x12, 12x5; n=4 matrices; m[i][j] stores min cost from i to j.

2. Step 2: Calculate minimum multiplication cost

   Length 2: m[1][2]=150, m[2][3]=360, m[3][4]=180
   Length 3: m[1][3]=min(0+360+600,150+0+180)=330; m[2][4]=min(0+180+150,360+0+600)=330
   Length 4: m[1][4]=min(0+330+250,150+180+75,330+0+300)=405

3. Final Answer:

   405 -> Option D

4. Quick Check:

   Minimum cost = 405 [OK]

Hint: Trace splits and pick minimum cost [OK]

Common Mistakes:

• Mixing up indices i, j, k
• Incorrect cost formula application
• Forgetting to initialize m[i][j] to max

4. Identify the error in the following C++ code snippet for Matrix Chain Multiplication and choose the correct fix:

int dims[] = {40, 20, 30, 10, 30};
int n = 4;
int m[5][5];
for (int i = 1; i <= n; i++)
  m[i][i] = 0;
for (int L = 2; L <= n; L++) {
  for (int i = 1; i <= n - L + 1; i++) {
    int j = i + L - 1;
    m[i][j] = 0;
    for (int k = i; k < j; k++) {
      int cost = m[i][k] + m[k+1][j] + dims[i-1]*dims[k]*dims[j];
      if (cost < m[i][j])
        m[i][j] = cost;
    }
  }
}

medium

A. Change dims indexing to dims[i-1]*dims[k]*dims[j]

B. Initialize m[i][j] to INT_MAX instead of 0 before inner loop

C. Start i loop from 0 instead of 1

D. Remove the if condition inside the inner loop

Solution

1. Step 1: Identify initialization mistake

   m[i][j] is set to 0 before checking for minimum cost, which causes incorrect comparisons.

2. Step 2: Correct initialization

   m[i][j] should be initialized to a very large number (INT_MAX) to find minimum cost correctly.

3. Final Answer:

   Initialize m[i][j] to INT_MAX instead of 0 before inner loop -> Option B

4. Quick Check:

   Use INT_MAX to find minimum cost [OK]

Hint: Initialize costs to max before minimizing [OK]

Common Mistakes:

• Setting m[i][j] to 0 instead of INT_MAX
• Incorrect dims indexing causing wrong cost
• Ignoring initialization leading to wrong min

5. Given matrices with dimensions 10x15, 15x20, 20x25, and 25x30, what is the minimum number of scalar multiplications needed to multiply the entire chain? Use the Matrix Chain Multiplication approach.

hard

A. 15500

B. 15000

C. 13000

D. 11250

Solution

1. Step 1: List matrix dimensions and possible splits

   Matrices: A1(10x15), A2(15x20), A3(20x25), A4(25x30). dims=[10,15,20,25,30]. Calculate costs for chain length 2 to 4.

2. Step 2: Calculate minimum costs for each subchain

   Length 2: m[1][2]=10*15*20=3000; m[2][3]=15*20*25=7500; m[3][4]=20*25*30=15000
   Length 3: m[1][3]=min(0+7500+10*15*25=11250, 3000+0+10*20*25=8000)=8000;
   m[2][4]=min(0+15000+15*20*30=24000, 7500+0+15*25*30=18750)=18750
   Length 4: m[1][4]=min(0+18750+10*15*30=23250, 3000+15000+10*20*30=24000, 8000+0+10*25*30=15500)=15500

3. Final Answer:

   15500 -> Option A

4. Quick Check:

   Minimum scalar multiplications = 15500 [OK]

Hint: Calculate costs for smaller chains first, then combine [OK]

Common Mistakes:

• Incorrectly calculating subproblems costs
• Mixing up matrix dimensions in cost formula
• Not checking all split points k