Skip to content

Latest commit

 

History

History
546 lines (458 loc) · 15 KB

README_EN.md

File metadata and controls

546 lines (458 loc) · 15 KB
comments difficulty edit_url tags
true
Hard
Array
Dynamic Programming

188. Best Time to Buy and Sell Stock IV

中文文档

Description

You are given an integer array prices where prices[i] is the price of a given stock on the ith day, and an integer k.

Find the maximum profit you can achieve. You may complete at most k transactions: i.e. you may buy at most k times and sell at most k times.

Note: You may not engage in multiple transactions simultaneously (i.e., you must sell the stock before you buy again).

 

Example 1:

Input: k = 2, prices = [2,4,1]
Output: 2
Explanation: Buy on day 1 (price = 2) and sell on day 2 (price = 4), profit = 4-2 = 2.

Example 2:

Input: k = 2, prices = [3,2,6,5,0,3]
Output: 7
Explanation: Buy on day 2 (price = 2) and sell on day 3 (price = 6), profit = 6-2 = 4. Then buy on day 5 (price = 0) and sell on day 6 (price = 3), profit = 3-0 = 3.

 

Constraints:

  • 1 <= k <= 100
  • 1 <= prices.length <= 1000
  • 0 <= prices[i] <= 1000

Solutions

Solution 1: Memoization Search

We design a function $dfs(i, j, k)$ to represent the maximum profit that can be obtained when starting from day $i$, completing at most $j$ transactions, and holding the stock with the current state $k$ (not holding the stock is represented by $0$, and holding the stock is represented by $1$). The answer is $dfs(0, k, 0)$.

The execution logic of the function $dfs(i, j, k)$ is as follows:

  • If $i$ is greater than or equal to $n$, return $0$ directly.
  • The i-th day can choose not to do anything, then $dfs(i, j, k) = dfs(i + 1, j, k)$.
  • If $k &gt; 0$, the i-th day can choose to sell the stock, then $dfs(i, j, k) = \max(dfs(i + 1, j - 1, 0) + prices[i], dfs(i + 1, j, k))$.
  • Otherwise, if $j &gt; 0$, the i-th day can choose to buy the stock, then $dfs(i, j, k) = \max(dfs(i + 1, j - 1, 1) - prices[i], dfs(i + 1, j, k))$.

The value of $dfs(i, j, k)$ is the maximum value of the above three cases.

During the process, we can use memoization search to save the results of each calculation to avoid repeated calculations.

The time complexity is $O(n \times k)$, and the space complexity is $O(n \times k)$, where $n$ and $k$ are the length of the prices array and the value of $k$, respectively.

Python3

class Solution:
    def maxProfit(self, k: int, prices: List[int]) -> int:
        @cache
        def dfs(i: int, j: int, k: int) -> int:
            if i >= len(prices):
                return 0
            ans = dfs(i + 1, j, k)
            if k:
                ans = max(ans, prices[i] + dfs(i + 1, j, 0))
            elif j:
                ans = max(ans, -prices[i] + dfs(i + 1, j - 1, 1))
            return ans

        return dfs(0, k, 0)

Java

class Solution {
    private Integer[][][] f;
    private int[] prices;
    private int n;

    public int maxProfit(int k, int[] prices) {
        n = prices.length;
        this.prices = prices;
        f = new Integer[n][k + 1][2];
        return dfs(0, k, 0);
    }

    private int dfs(int i, int j, int k) {
        if (i >= n) {
            return 0;
        }
        if (f[i][j][k] != null) {
            return f[i][j][k];
        }
        int ans = dfs(i + 1, j, k);
        if (k > 0) {
            ans = Math.max(ans, prices[i] + dfs(i + 1, j, 0));
        } else if (j > 0) {
            ans = Math.max(ans, -prices[i] + dfs(i + 1, j - 1, 1));
        }
        return f[i][j][k] = ans;
    }
}

C++

class Solution {
public:
    int maxProfit(int k, vector<int>& prices) {
        int n = prices.size();
        int f[n][k + 1][2];
        memset(f, -1, sizeof(f));
        function<int(int, int, int)> dfs = [&](int i, int j, int k) -> int {
            if (i >= n) {
                return 0;
            }
            if (f[i][j][k] != -1) {
                return f[i][j][k];
            }
            int ans = dfs(i + 1, j, k);
            if (k) {
                ans = max(ans, prices[i] + dfs(i + 1, j, 0));
            } else if (j) {
                ans = max(ans, -prices[i] + dfs(i + 1, j - 1, 1));
            }
            return f[i][j][k] = ans;
        };
        return dfs(0, k, 0);
    }
};

Go

func maxProfit(k int, prices []int) int {
	n := len(prices)
	f := make([][][2]int, n)
	for i := range f {
		f[i] = make([][2]int, k+1)
		for j := range f[i] {
			f[i][j] = [2]int{-1, -1}
		}
	}
	var dfs func(i, j, k int) int
	dfs = func(i, j, k int) int {
		if i >= n {
			return 0
		}
		if f[i][j][k] != -1 {
			return f[i][j][k]
		}
		ans := dfs(i+1, j, k)
		if k > 0 {
			ans = max(ans, prices[i]+dfs(i+1, j, 0))
		} else if j > 0 {
			ans = max(ans, -prices[i]+dfs(i+1, j-1, 1))
		}
		f[i][j][k] = ans
		return ans
	}
	return dfs(0, k, 0)
}

TypeScript

function maxProfit(k: number, prices: number[]): number {
    const n = prices.length;
    const f = Array.from({ length: n }, () =>
        Array.from({ length: k + 1 }, () => Array.from({ length: 2 }, () => -1)),
    );
    const dfs = (i: number, j: number, k: number): number => {
        if (i >= n) {
            return 0;
        }
        if (f[i][j][k] !== -1) {
            return f[i][j][k];
        }
        let ans = dfs(i + 1, j, k);
        if (k) {
            ans = Math.max(ans, prices[i] + dfs(i + 1, j, 0));
        } else if (j) {
            ans = Math.max(ans, -prices[i] + dfs(i + 1, j - 1, 1));
        }
        return (f[i][j][k] = ans);
    };
    return dfs(0, k, 0);
}

C#

public class Solution {
    private int[,,] f;
    private int[] prices;
    private int n;

    public int MaxProfit(int k, int[] prices) {
        n = prices.Length;
        f = new int[n, k + 1, 2];
        this.prices = prices;
        for (int i = 0; i < n; ++i) {
            for (int j = 0; j <= k; ++j) {
                f[i, j, 0] = -1;
                f[i, j, 1] = -1;
            }
        }
        return dfs(0, k, 0);
    }

    private int dfs(int i, int j, int k) {
        if (i >= n) {
            return 0;
        }
        if (f[i, j, k] != -1) {
            return f[i, j, k];
        }
        int ans = dfs(i + 1, j, k);
        if (k > 0) {
            ans = Math.Max(ans, prices[i] + dfs(i + 1, j, 0));
        }
        else if (j > 0) {
            ans = Math.Max(ans, -prices[i] + dfs(i + 1, j - 1, 1));
        }
        return f[i, j, k] = ans;
    }
}

Solution 2: Dynamic Programming

We can also use dynamic programming to define $f[i][j][k]$ as the maximum profit that can be obtained when completing at most j transactions (here we define the number of transactions as the number of purchases), and holding the stock with the current state k on the i-th day. The initial value of $f[i][j][k]$ is 0. The answer is $f[n - 1][k][0]$.

When $i = 0$, the stock price is $prices[0]$. For any $j$ \in [1, k]$, we have $f[0][j][1] = -prices[0]$, which means buying the stock on the 0-th day with a profit of $-prices[0]$.

When $i &gt; 0$:

  • If the i-th day does not hold the stock, it may be that the stock was held on the i-1-th day and sold on the i-th day, or the stock was not held on the i-1-th day and no operation was performed on the i-th day. Therefore, $f[i][j][0] = \max(f[i - 1][j][1] + prices[i], f[i - 1][j][0])$.
  • If the i-th day holds the stock, it may be that the stock was not held on the i-1-th day and bought on the i-th day, or the stock was held on the i-1-th day and no operation was performed on the i-th day. Therefore, $f[i][j][1] = max(f[i - 1][j - 1][0] - prices[i], f[i - 1][j][1])$.

Therefore, when $i &gt; 0$, we can get the state transition equation:

$$ \begin{aligned} f[i][j][0] &= \max(f[i - 1][j][1] + prices[i], f[i - 1][j][0]) \\ f[i][j][1] &= \max(f[i - 1][j - 1][0] - prices[i], f[i - 1][j][1]) \end{aligned} $$

The final answer is $f[n - 1][k][0]$.

The time complexity is $O(n \times k)$, and the space complexity is $O(n \times k)$, where $n$ and $k$ are the length of the prices array and the value of $k$, respectively.

We notice that the state $f[i][]$ only depends on the state $f[i - 1][]$, so we can optimize the first dimension of the space and reduce the space complexity to $O(k)$.

Python3

class Solution:
    def maxProfit(self, k: int, prices: List[int]) -> int:
        n = len(prices)
        f = [[[0] * 2 for _ in range(k + 1)] for _ in range(n)]
        for j in range(1, k + 1):
            f[0][j][1] = -prices[0]
        for i, x in enumerate(prices[1:], 1):
            for j in range(1, k + 1):
                f[i][j][0] = max(f[i - 1][j][1] + x, f[i - 1][j][0])
                f[i][j][1] = max(f[i - 1][j - 1][0] - x, f[i - 1][j][1])
        return f[n - 1][k][0]

Java

class Solution {
    public int maxProfit(int k, int[] prices) {
        int n = prices.length;
        int[][][] f = new int[n][k + 1][2];
        for (int j = 1; j <= k; ++j) {
            f[0][j][1] = -prices[0];
        }
        for (int i = 1; i < n; ++i) {
            for (int j = 1; j <= k; ++j) {
                f[i][j][0] = Math.max(f[i - 1][j][1] + prices[i], f[i - 1][j][0]);
                f[i][j][1] = Math.max(f[i - 1][j - 1][0] - prices[i], f[i - 1][j][1]);
            }
        }
        return f[n - 1][k][0];
    }
}

C++

class Solution {
public:
    int maxProfit(int k, vector<int>& prices) {
        int n = prices.size();
        int f[n][k + 1][2];
        memset(f, 0, sizeof(f));
        for (int j = 1; j <= k; ++j) {
            f[0][j][1] = -prices[0];
        }
        for (int i = 1; i < n; ++i) {
            for (int j = 1; j <= k; ++j) {
                f[i][j][0] = max(f[i - 1][j][1] + prices[i], f[i - 1][j][0]);
                f[i][j][1] = max(f[i - 1][j - 1][0] - prices[i], f[i - 1][j][1]);
            }
        }
        return f[n - 1][k][0];
    }
};

Go

func maxProfit(k int, prices []int) int {
	n := len(prices)
	f := make([][][2]int, n)
	for i := range f {
		f[i] = make([][2]int, k+1)
	}
	for j := 1; j <= k; j++ {
		f[0][j][1] = -prices[0]
	}
	for i := 1; i < n; i++ {
		for j := 1; j <= k; j++ {
			f[i][j][0] = max(f[i-1][j][1]+prices[i], f[i-1][j][0])
			f[i][j][1] = max(f[i-1][j-1][0]-prices[i], f[i-1][j][1])
		}
	}
	return f[n-1][k][0]
}

TypeScript

function maxProfit(k: number, prices: number[]): number {
    const n = prices.length;
    const f = Array.from({ length: n }, () =>
        Array.from({ length: k + 1 }, () => Array.from({ length: 2 }, () => 0)),
    );
    for (let j = 1; j <= k; ++j) {
        f[0][j][1] = -prices[0];
    }
    for (let i = 1; i < n; ++i) {
        for (let j = 1; j <= k; ++j) {
            f[i][j][0] = Math.max(f[i - 1][j][1] + prices[i], f[i - 1][j][0]);
            f[i][j][1] = Math.max(f[i - 1][j - 1][0] - prices[i], f[i - 1][j][1]);
        }
    }
    return f[n - 1][k][0];
}

C#

public class Solution {
    public int MaxProfit(int k, int[] prices) {
        int n = prices.Length;
        int[,,] f = new int[n, k + 1, 2];
        for (int j = 1; j <= k; ++j) {
            f[0, j, 1] = -prices[0];
        }
        for (int i = 1; i < n; ++i) {
            for (int j = 1; j <= k; ++j) {
                f[i, j, 0] = Math.Max(f[i - 1, j, 1] + prices[i], f[i - 1, j, 0]);
                f[i, j, 1] = Math.Max(f[i - 1, j - 1, 0] - prices[i], f[i - 1, j, 1]);
            }
        }
        return f[n - 1, k, 0];
    }
}

Solution 3

Python3

class Solution:
    def maxProfit(self, k: int, prices: List[int]) -> int:
        f = [[0] * 2 for _ in range(k + 1)]
        for j in range(1, k + 1):
            f[j][1] = -prices[0]
        for x in prices[1:]:
            for j in range(k, 0, -1):
                f[j][0] = max(f[j][1] + x, f[j][0])
                f[j][1] = max(f[j - 1][0] - x, f[j][1])
        return f[k][0]

Java

class Solution {
    public int maxProfit(int k, int[] prices) {
        int n = prices.length;
        int[][] f = new int[k + 1][2];
        for (int j = 1; j <= k; ++j) {
            f[j][1] = -prices[0];
        }
        for (int i = 1; i < n; ++i) {
            for (int j = k; j > 0; --j) {
                f[j][0] = Math.max(f[j][1] + prices[i], f[j][0]);
                f[j][1] = Math.max(f[j - 1][0] - prices[i], f[j][1]);
            }
        }
        return f[k][0];
    }
}

C++

class Solution {
public:
    int maxProfit(int k, vector<int>& prices) {
        int n = prices.size();
        int f[k + 1][2];
        memset(f, 0, sizeof(f));
        for (int j = 1; j <= k; ++j) {
            f[j][1] = -prices[0];
        }
        for (int i = 1; i < n; ++i) {
            for (int j = k; j; --j) {
                f[j][0] = max(f[j][1] + prices[i], f[j][0]);
                f[j][1] = max(f[j - 1][0] - prices[i], f[j][1]);
            }
        }
        return f[k][0];
    }
};

Go

func maxProfit(k int, prices []int) int {
	f := make([][2]int, k+1)
	for j := 1; j <= k; j++ {
		f[j][1] = -prices[0]
	}
	for _, x := range prices[1:] {
		for j := k; j > 0; j-- {
			f[j][0] = max(f[j][1]+x, f[j][0])
			f[j][1] = max(f[j-1][0]-x, f[j][1])
		}
	}
	return f[k][0]
}

TypeScript

function maxProfit(k: number, prices: number[]): number {
    const f = Array.from({ length: k + 1 }, () => Array.from({ length: 2 }, () => 0));
    for (let j = 1; j <= k; ++j) {
        f[j][1] = -prices[0];
    }
    for (const x of prices.slice(1)) {
        for (let j = k; j; --j) {
            f[j][0] = Math.max(f[j][1] + x, f[j][0]);
            f[j][1] = Math.max(f[j - 1][0] - x, f[j][1]);
        }
    }
    return f[k][0];
}

C#

public class Solution {
    public int MaxProfit(int k, int[] prices) {
        int n = prices.Length;
        int[,] f = new int[k + 1, 2];
        for (int j = 1; j <= k; ++j) {
            f[j, 1] = -prices[0];
        }
        for (int i = 1; i < n; ++i) {
            for (int j = k; j > 0; --j) {
                f[j, 0] = Math.Max(f[j, 1] + prices[i], f[j, 0]);
                f[j, 1] = Math.Max(f[j - 1, 0] - prices[i], f[j, 1]);
            }
        }
        return f[k, 0];
    }
}