# Posts tagged as “dp”

A parentheses string is a non-empty string consisting only of '(' and ')'. It is valid if any of the following conditions is true:

• It is ().
• It can be written as AB (A concatenated with B), where A and B are valid parentheses strings.
• It can be written as (A), where A is a valid parentheses string.

You are given an m x n matrix of parentheses grid. A valid parentheses string path in the grid is a path satisfying all of the following conditions:

• The path starts from the upper left cell (0, 0).
• The path ends at the bottom-right cell (m - 1, n - 1).
• The path only ever moves down or right.
• The resulting parentheses string formed by the path is valid.

Return true if there exists a valid parentheses string path in the grid. Otherwise, return false.

Example 1:

Input: grid = [["(","(","("],[")","(",")"],["(","(",")"],["(","(",")"]]
Output: true
Explanation: The above diagram shows two possible paths that form valid parentheses strings.
The first path shown results in the valid parentheses string "()(())".
The second path shown results in the valid parentheses string "((()))".
Note that there may be other valid parentheses string paths.


Example 2:

Input: grid = [[")",")"],["(","("]]
Output: false
Explanation: The two possible paths form the parentheses strings "))(" and ")((". Since neither of them are valid parentheses strings, we return false.


Constraints:

• m == grid.length
• n == grid[i].length
• 1 <= m, n <= 100
• grid[i][j] is either '(' or ')'.

## Solution: DP

Let dp(i, j, b) denote whether there is a path from (i,j) to (m-1, n-1) given b open parentheses.
if we are at (m – 1, n – 1) and b == 0 then we found a valid path.
dp(i, j, b) = dp(i + 1, j, b’) or dp(i, j + 1, b’) where b’ = b + 1 if grid[i][j] == ‘(‘ else -1

Time complexity: O(m*n*(m + n))
Space complexity: O(m*n*(m + n))

## Python3

Alice is texting Bob using her phone. The mapping of digits to letters is shown in the figure below.

In order to add a letter, Alice has to press the key of the corresponding digit i times, where i is the position of the letter in the key.

• For example, to add the letter 's', Alice has to press '7' four times. Similarly, to add the letter 'k', Alice has to press '5' twice.
• Note that the digits '0' and '1' do not map to any letters, so Alice does not use them.

However, due to an error in transmission, Bob did not receive Alice’s text message but received a string of pressed keys instead.

• For example, when Alice sent the message "bob", Bob received the string "2266622".

Given a string pressedKeys representing the string received by Bob, return the total number of possible text messages Alice could have sent.

Since the answer may be very large, return it modulo 109 + 7.

Example 1:

Input: pressedKeys = "22233"
Output: 8
Explanation:
The possible text messages Alice could have sent are:
Since there are 8 possible messages, we return 8.


Example 2:

Input: pressedKeys = "222222222222222222222222222222222222"
Output: 82876089
Explanation:
There are 2082876103 possible text messages Alice could have sent.
Since we need to return the answer modulo 109 + 7, we return 2082876103 % (109 + 7) = 82876089.


Constraints:

• 1 <= pressedKeys.length <= 105
• pressedKeys only consists of digits from '2' – '9'.

## Solution: DP

Similar to 花花酱 LeetCode 91. Decode Ways, let dp[i] denote # of possible messages of substr s[i:]

dp[i] = dp[i + 1]
+ dp[i + 2] (if s[i:i+1] are the same)
+ dp[i + 3] (if s[i:i+2] are the same)
+ dp[i + 4] (if s[i:i+3] are the same and s[i] in ’79’)

dp[n] = 1

Time complexity: O(n)
Space complexity: O(n) -> O(4)

## Python3

You are given a 0-indexed binary string s which represents the types of buildings along a street where:

• s[i] = '0' denotes that the ith building is an office and
• s[i] = '1' denotes that the ith building is a restaurant.

As a city official, you would like to select 3 buildings for random inspection. However, to ensure variety, no two consecutive buildings out of the selected buildings can be of the same type.

• For example, given s = "001101", we cannot select the 1st3rd, and 5th buildings as that would form "011" which is not allowed due to having two consecutive buildings of the same type.

Return the number of valid ways to select 3 buildings.

Example 1:

Input: s = "001101"
Output: 6
Explanation:
The following sets of indices selected are valid:
- [0,2,4] from "001101" forms "010"
- [0,3,4] from "001101" forms "010"
- [1,2,4] from "001101" forms "010"
- [1,3,4] from "001101" forms "010"
- [2,4,5] from "001101" forms "101"
- [3,4,5] from "001101" forms "101"
No other selection is valid. Thus, there are 6 total ways.


Example 2:

Input: s = "11100"
Output: 0
Explanation: It can be shown that there are no valid selections.


Constraints:

• 3 <= s.length <= 105
• s[i] is either '0' or '1'.

## Solution: DP

The brute force solution will take O(n3) which will lead to TLE.

Since the only two valid cases are “010” and “101”.

We just need to count how many 0s and 1s, thus we can count 01s and 10s and finally 010s and 101s.

## C++

There are n piles of coins on a table. Each pile consists of a positive number of coins of assorted denominations.

In one move, you can choose any coin on top of any pile, remove it, and add it to your wallet.

Given a list piles, where piles[i] is a list of integers denoting the composition of the ith pile from top to bottom, and a positive integer k, return the maximum total value of coins you can have in your wallet if you choose exactly k coins optimally.

Example 1:

Input: piles = [[1,100,3],[7,8,9]], k = 2
Output: 101
Explanation:
The above diagram shows the different ways we can choose k coins.
The maximum total we can obtain is 101.


Example 2:

Input: piles = [[100],[100],[100],[100],[100],[100],[1,1,1,1,1,1,700]], k = 7
Output: 706
Explanation:
The maximum total can be obtained if we choose all coins from the last pile.


Constraints:

• n == piles.length
• 1 <= n <= 1000
• 1 <= piles[i][j] <= 105
• 1 <= k <= sum(piles[i].length) <= 2000

## Solution: DP

let dp(i, k) be the maximum value of picking k elements using piles[i:n].

dp(i, k) = max(dp(i + 1, k), sum(piles[i][0~j]) + dp(i + 1, k – j – 1)), 0 <= j < len(piles[i])

Time complexity: O(n * m), m = sum(piles[i]) <= 2000
Space complexity: O(n * k)

## Python

You are given a 0-indexed 2D integer array tires where tires[i] = [fi, ri] indicates that the ith tire can finish its xth successive lap in fi * ri(x-1) seconds.

• For example, if fi = 3 and ri = 2, then the tire would finish its 1st lap in 3 seconds, its 2nd lap in 3 * 2 = 6 seconds, its 3rd lap in 3 * 22 = 12 seconds, etc.

You are also given an integer changeTime and an integer numLaps.

The race consists of numLaps laps and you may start the race with any tire. You have an unlimited supply of each tire and after every lap, you may change to any given tire (including the current tire type) if you wait changeTime seconds.

Return the minimum time to finish the race.

Example 1:

Input: tires = [[2,3],[3,4]], changeTime = 5, numLaps = 4
Output: 21
Explanation:
Lap 1: Start with tire 0 and finish the lap in 2 seconds.
Lap 2: Continue with tire 0 and finish the lap in 2 * 3 = 6 seconds.
Lap 3: Change tires to a new tire 0 for 5 seconds and then finish the lap in another 2 seconds.
Lap 4: Continue with tire 0 and finish the lap in 2 * 3 = 6 seconds.
Total time = 2 + 6 + 5 + 2 + 6 = 21 seconds.
The minimum time to complete the race is 21 seconds.


Example 2:

Input: tires = [[1,10],[2,2],[3,4]], changeTime = 6, numLaps = 5
Output: 25
Explanation:
Lap 1: Start with tire 1 and finish the lap in 2 seconds.
Lap 2: Continue with tire 1 and finish the lap in 2 * 2 = 4 seconds.
Lap 3: Change tires to a new tire 1 for 6 seconds and then finish the lap in another 2 seconds.
Lap 4: Continue with tire 1 and finish the lap in 2 * 2 = 4 seconds.
Lap 5: Change tires to tire 0 for 6 seconds then finish the lap in another 1 second.
Total time = 2 + 4 + 6 + 2 + 4 + 6 + 1 = 25 seconds.
The minimum time to complete the race is 25 seconds.


Constraints:

• 1 <= tires.length <= 105
• tires[i].length == 2
• 1 <= fi, changeTime <= 105
• 2 <= ri <= 105
• 1 <= numLaps <= 1000

## Solution: DP

Observation: since ri >= 2, we must change tire within 20 laps, otherwise it will be slower.

pre-compute the time to finish k laps using each type of tire (k < 20), find min for each lap.

dp[i] = best[i], i < 20,
dp[i] = min{dp[i – j] + changeTime + best[j]}, i > 20

Time complexity: O(n)
Space complexity: O(n) -> O(20)