Matrix Chain Multiplication

Problem

Given a sequence of matrices, find the most efficient way to multiply these matrices together. The problem is not to perform the multiplications, but to decide the order in which to multiply the matrices to minimize the total number of scalar multiplications.

Matrix multiplication is associative, so the result does not depend on how the matrices are parenthesized, but the number of operations does. Given an array p[] of length n+1, where the ith matrix has dimensions p[i-1] x p[i], write a function MatrixChainOrder() that returns the minimum number of multiplications needed to multiply the chain.

Examples

Example 1

Input: p = [40, 20, 30, 10, 30]
Output: 26000
Explanation: There are 4 matrices of dimensions 40x20, 20x30, 30x10, and 10x30.
The minimum number of multiplications is obtained by parenthesizing as (A(BC))D.
Calculation: 20*30*10 + 40*20*10 + 40*10*30 = 6000 + 8000 + 12000 = 26000

Example 2

Input: p = [10, 20, 30, 40, 30]
Output: 30000
Explanation: There are 4 matrices of dimensions 10x20, 20x30, 30x40, and 40x30.
The minimum number of multiplications is obtained by parenthesizing as ((AB)C)D.
Calculation: 10*20*30 + 10*30*40 + 10*40*30 = 6000 + 12000 + 12000 = 30000

Example 3

Input: p = [10, 20, 30]
Output: 6000
Explanation: Only two matrices: 10x20 and 20x30. Only one way to multiply: 10*20*30 = 6000

Method 1 - Naive

Intuition

The naive approach tries every possible way to parenthesize the product and recursively calculates the cost for each way. It chooses the minimum among all possible parenthesizations.

Approach

For a chain of matrices from index i to j, try placing a parenthesis at every possible position k between i and j.
Recursively compute the minimum cost for multiplying the left and right subchains.
Add the cost of multiplying the two resulting matrices.
Base case: if there is only one matrix (i == j), no multiplication is needed.

Code

C++

int matrixChain(const std::vector<int>& p, int i, int j) {
    if (i == j) return 0;
    int minCost = INT_MAX;
    for (int k = i; k < j; ++k) {
        int cost = matrixChain(p, i, k)
                 + matrixChain(p, k + 1, j)
                 + p[i - 1] * p[k] * p[j];
        minCost = std::min(minCost, cost);
    }
    return minCost;
}
// Usage: matrixChain(p, 1, n-1)

Java

int matrixChain(int[] p, int i, int j) {
    if (i == j) return 0;
    int min = Integer.MAX_VALUE;
    for (int k = i; k < j; k++) {
        int cost = matrixChain(p, i, k)
                 + matrixChain(p, k + 1, j)
                 + p[i - 1] * p[k] * p[j];
        if (cost < min) min = cost;
    }
    return min;
}
// Usage: matrixChain(p, 1, n-1)

Python

def matrixChain(p, i, j):
    if i == j:
        return 0
    min_cost = float('inf')
    for k in range(i, j):
        cost = (matrixChain(p, i, k)
                + matrixChain(p, k + 1, j)
                + p[i - 1] * p[k] * p[j])
        min_cost = min(min_cost, cost)
    return min_cost
# Usage: matrixChain(p, 1, len(p)-1)

Complexity

⏰ Time complexity: O(2^n) (exponential, as all possible parenthesizations are tried)
🧺 Space complexity: O(n) (due to recursion stack)

Method 2 - Top Down DP

Intuition

The naive approach recalculates the same subproblems multiple times. Top-down DP (memoization) stores the results of subproblems in a table, so each subproblem is solved only once.

Optimal Substructure

The Matrix Chain Multiplication problem can be broken down into smaller subproblems. For a sequence of matrices, the goal is to determine the most efficient way to multiply them by placing parentheses in different positions. For a chain of n matrices (A, B, C, D), you can split the multiplication at any position, resulting in subchains like:

(A) × (BCD)
(AB) × (CD)
(ABC) × (D)

Each split divides the problem into two smaller matrix chain multiplication problems. The minimum number of scalar multiplications needed for the whole chain is the minimum among all possible splits, where each split’s cost is the sum of the costs of multiplying the left and right subchains plus the cost of multiplying the resulting two matrices. This recursive structure demonstrates the optimal substructure property.

Example

A (10×20), B (20×30), C (30×40), D (40×30). So, p = [10, 20, 30, 40, 30]

We want to compute the minimum number of scalar multiplications needed to multiply A × B × C × D.

graph TD
    A1["matrixChain(1,4)"]
    B1["matrixChain(1,1)"]
    B2["matrixChain(2,4)"]
    C1["matrixChain(2,2)"]
    C2["matrixChain(3,4)"]
    D1["matrixChain(3,3)"]
    D2["matrixChain(4,4)"]
    C3["matrixChain(2,3)"]
    D3["matrixChain(2,2)"]
    D4["matrixChain(3,3)"]
    B3["matrixChain(1,2)"]
    C4["matrixChain(1,1)"]
    C5["matrixChain(2,2)"]
    B4["matrixChain(1,3)"]
    C6["matrixChain(1,1)"]
    C7["matrixChain(2,3)"]
    D5["matrixChain(2,2)"]
    D6["matrixChain(3,3)"]

    A1 --> B1
    A1 --> B2
    B2 --> C1
    B2 --> C2
    C2 --> D1
    C2 --> D2
    B2 --> C3
    C3 --> D3
    C3 --> D4
    A1 --> B3
    B3 --> C4
    B3 --> C5
    A1 --> B4
    B4 --> C6
    B4 --> C7
    C7 --> D5
    C7 --> D6

Suppose you have matrices A, B, C, D. To find the minimum multiplication cost, you can split the chain at different positions:

First split: (A) × (BCD)
Second split: (AB) × (CD)
Third split: (ABC) × (D)

Each split leads to recursive calls for the left and right segments. For instance, the subproblem of multiplying (B × C × D) may be encountered in both the first and second splits, leading to redundant calculations if not stored and reused.

Approach

Use a 2D memoization table dp[i][j] to store the minimum cost for multiplying matrices from i to j.
If the value is already computed, return it.
Otherwise, compute recursively as in the naive approach, but store and reuse results.

Code

C++

int matrixChain(const std::vector<int>& p, int i, int j, std::vector<std::vector<int>>& dp) {
    if (i == j) return 0;
    if (dp[i][j] != -1) return dp[i][j];
    int minCost = INT_MAX;
    for (int k = i; k < j; ++k) {
        int cost = matrixChain(p, i, k, dp)
                 + matrixChain(p, k + 1, j, dp)
                 + p[i - 1] * p[k] * p[j];
        minCost = std::min(minCost, cost);
    }
    dp[i][j] = minCost;
    return minCost;
}
// Usage:
// int n = p.size();
// std::vector<std::vector<int>> dp(n, std::vector<int>(n, -1));
// matrixChain(p, 1, n-1, dp);

Java

int matrixChain(int[] p, int i, int j, int[][] dp) {
    if (i == j) return 0;
    if (dp[i][j] != -1) return dp[i][j];
    int min = Integer.MAX_VALUE;
    for (int k = i; k < j; k++) {
        int cost = matrixChain(p, i, k, dp)
                 + matrixChain(p, k + 1, j, dp)
                 + p[i - 1] * p[k] * p[j];
        if (cost < min) min = cost;
    }
    dp[i][j] = min;
    return min;
}
// Usage:
// int n = p.length;
// int[][] dp = new int[n][n];
// for (int[] row : dp) Arrays.fill(row, -1);
// matrixChain(p, 1, n-1, dp);

Python

def matrixChain(p, i, j, dp):
    if i == j:
        return 0
    if dp[i][j] != -1:
        return dp[i][j]
    min_cost = float('inf')
    for k in range(i, j):
        cost = (matrixChain(p, i, k, dp)
                + matrixChain(p, k + 1, j, dp)
                + p[i - 1] * p[k] * p[j])
        min_cost = min(min_cost, cost)
    dp[i][j] = min_cost
    return min_cost
# Usage:
# n = len(p)
# dp = [[-1]*n for _ in range(n)]
# matrixChain(p, 1, n-1, dp)

Complexity

⏰ Time complexity: O(n^3) (since there are O(n^2) subproblems and each takes O(n) time)
🧺 Space complexity: O(n^2) (for the memoization table)

Method 3 - Bottom up DP

Intuition

Bottom-up DP builds the solution iteratively by solving smaller subproblems first and using their results to solve larger subproblems. We fill a DP table where dp[i][j] stores the minimum cost to multiply matrices from i to j.

Approach

Initialize a 2D table dp of size n x n with zeros on the diagonal (cost of multiplying one matrix is zero).
For chain lengths from 2 to n-1, compute the minimum cost for all subchains.
For each subchain (i, j), try all possible splits k and update dp[i][j] with the minimum cost.
The answer is in dp[1][n-1].

Code

C++

int matrixChainOrder(const std::vector<int>& p) {
    int n = p.size();
    std::vector<std::vector<int>> dp(n, std::vector<int>(n, 0));
    for (int len = 2; len < n; ++len) {
        for (int i = 1; i < n - len + 1; ++i) {
            int j = i + len - 1;
            dp[i][j] = INT_MAX;
            for (int k = i; k < j; ++k) {
                int cost = dp[i][k] + dp[k+1][j] + p[i-1]*p[k]*p[j];
                dp[i][j] = std::min(dp[i][j], cost);
            }
        }
    }
    return dp[1][n-1];
}

Java

int matrixChainOrder(int[] p) {
    int n = p.length;
    int[][] dp = new int[n][n];
    for (int len = 2; len < n; len++) {
        for (int i = 1; i < n - len + 1; i++) {
            int j = i + len - 1;
            dp[i][j] = Integer.MAX_VALUE;
            for (int k = i; k < j; k++) {
                int cost = dp[i][k] + dp[k+1][j] + p[i-1]*p[k]*p[j];
                dp[i][j] = Math.min(dp[i][j], cost);
            }
        }
    }
    return dp[1][n-1];
}

Python

def matrixChainOrder(p):
    n = len(p)
    dp = [[0]*n for _ in range(n)]
    for length in range(2, n):
        for i in range(1, n - length + 1):
            j = i + length - 1
            dp[i][j] = float('inf')
            for k in range(i, j):
                cost = dp[i][k] + dp[k+1][j] + p[i-1]*p[k]*p[j]
                dp[i][j] = min(dp[i][j], cost)
    return dp[1][n-1]

Complexity

⏰ Time complexity: O(n^3)
🧺 Space complexity: O(n^2)

Dry Run

Let's fill the DP table for this example (p = [10, 20, 30, 40, 30], n = 5):

	0	1	2	3	4
0	0
1		0
2			0
3				0
4					0

Now, fill for chain length 2:

dp[1][2] = 10×20×30 = 6000
dp[2][3] = 20×30×40 = 24000
dp[3][4] = 30×40×30 = 36000

	0	1	2	3	4
0	0
1		0	6000
2			0	24000
3				0	36000
4					0

Chain length 3:

dp[1][3] = min( (10×20×40 + 0 + 24000) = 32000, (6000 + 10×30×40 + 0) = 18000 ) = 18000
dp[2][4] = min( (20×30×30 + 0 + 36000) = 54000, (24000 + 20×40×30 + 0) = 48000 ) = 48000

	0	1	2	3	4
0	0
1		0	6000	18000
2			0	24000	48000
3				0	36000
4					0

Chain length 4:

dp[1][4] = min( (10×20×30 + 0 + 48000) = 54000, (6000 + 10×30×30 + 36000) = 51000, (18000 + 10×40×30 + 0) = 30000 ) = 30000

	0	1	2	3	4
0	0
1		0	6000	18000	30000
2			0	24000	48000
3				0	36000
4					0

Final answer: Minimum number of multiplications = 30000 (dp[1][4])

Matrix Chain Multiplication

Problem

Examples

Example 1

Example 2

Example 3

Similar Problems

Method 1 - Naive

Intuition

Approach

Code

C++

Java

Python

Complexity

Method 2 - Top Down DP

Intuition

Optimal Substructure

Example

Approach

Code

C++

Java

Python

Complexity

Method 3 - Bottom up DP

Intuition

Approach

Code

C++

Java

Python

Complexity

Dry Run

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