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1770. Maximum Score from Performing Multiplication Operations

You are given two integer arrays nums and multipliers of size n and m respectively, where n >= m. The arrays are 1-indexed.

You begin with a score of 0. You want to perform exactly m operations. On the ith operation (1-indexed), you will:

Choose one integer x from either the start or the end of the array nums.

Add multipliers[i] * x to your score.

Remove x from the array nums.

Return the maximum score after performing m operations.

Example 1:

Input: nums = [1,2,3], multipliers = [3,2,1]

Output: 14

Explanation: An optimal solution is as follows:

- Choose from the end, [1,2,3], adding 3 * 3 = 9 to the score.

- Choose from the end, [1,2], adding 2 * 2 = 4 to the score.

- Choose from the end, [1], adding 1 * 1 = 1 to the score.

The total score is 9 + 4 + 1 = 14.

Example 2:

Input: nums = [-5,-3,-3,-2,7,1], multipliers = [-10,-5,3,4,6]

Output: 102

Explanation: An optimal solution is as follows:

- Choose from the start, [-5,-3,-3,-2,7,1], adding -5 * -10 = 50 to the score.

- Choose from the start, [-3,-3,-2,7,1], adding -3 * -5 = 15 to the score.

- Choose from the start, [-3,-2,7,1], adding -3 * 3 = -9 to the score.

- Choose from the end, [-2,7,1], adding 1 * 4 = 4 to the score.

- Choose from the end, [-2,7], adding 7 * 6 = 42 to the score.

The total score is 50 + 15 - 9 + 4 + 42 = 102.

Constraints:

n == nums.length

m == multipliers.length

1 <= m <= 103

m <= n <= 105

-1000 <= nums[i], multipliers[i] <= 1000

[TOC]

Solution

Overview

As per the problem description, we have to do m operations and find the maximum score. At every operation, we have to select $i^{th}$ integer from multipliers and multiply it with an integer x from nums. The integer x can be chosen from either the start or the end of the nums. And then we have to remove that integer from nums.

At first glance, a greedy approach looks promising. In step i, out of nums[start] and nums[end], we can pick the integer x that maximizes x * multipliers[i].

This greedy approach works well for one of the given examples.

nums = [1,2,3], multipliers = [3,2,1]

Taking Decision

‣ From multipliers, we have 3, nums is [1, 2, 3], from 3 * 1 and 3 * 3, pick 3, add 3 * 3 = 9.

‣ From multipliers, we have 2, nums is [1, 2], from 2 * 1 and 2 * 2, pick 2, add 2 * 2 = 4.

‣ From multipliers, we have 1, nums is [1], add 1 * 1 = 1.

Total Score is 9+4+1=14, which is correct

However, it fails for the second example.

nums = [-5,-3,-3,-2,7,1], multipliers = [-10,-5,3,4,6]

Taking Decision

‣ From multipliers, we have 10, nums is [-5,-3,-3,-2,7,1], from (-10) * (-5) and (-10) * 1, pick -5, add (-10) * (-5) = 50.

‣ From multipliers, we have -5, nums is [-3,-3,-2,7,1], from (-5) * (-3) and (-5) * 1, pick -3, add (-5) * (-3) = 15.

‣ From multipliers, we have 3, nums is [-3,-2,7,1], from 3 * (-3) and 3 * 1, pick 1, add 3 * 1 = 3.

‣ From multipliers, we have 4, nums is [-3,-2,7], from 4 * (-3) and 4 * 7, pick 7, add 4 * 7 = 28.

‣ From multipliers, we have 6, nums is [-3,-2], from 6 * (-3) and 6 * (-2), pick -2, add 6 * (-2) = -12.

Total Score is 50+15+3+28+(-12)=84 which isn't optimal.

102 is Optimal Solution as given in Problem Example.

The logical intuition of why it is not optimal can be deduced from the following two cases:

Greedy is short-sighted. For global optimum, we pick local optimum. But picking this Local Optimum may restrict greater positive product afterward.

nums = [-10,1,1000,1,1,100], multipliers = [1,1,1]

If we pick 100 over -10, we would never ever be able to collect 1000. There are only three elements in multipliers, and we can collect 1000 by taking the left integers only. But selecting 100 at the first point restricts it.

Moreover, what if both ends of nums are identical? We don't know which one to favor. One may yield another score, another may yield a very different score.

nums = [2, 1000, -1, 2], multipliers = [1, 1]

a. if we select Left 2 first, then at the next step, there would be a contest between Left 1000 and Right 2. As per approach we now would select left 1000, obtaining 1002 as the answer.

b. if we select Right 2 first, then at the next step, there would be a contest between Left 2 and Right -1. As per approach we now would select left 2, obtaining 4 as the answer.

Thus, these examples suggest that we have to look for all two possible combinations at each step:

Select nums[start], now problem reduces to another subproblem with nums being nums[start+1] to nums[end] and multipliers being multipliers[i+1] to multipliers[m-1]. Moreover, the number of operations is lessened by one.

Select nums[end], now problem reduces to another subproblem with nums being nums[start] to nums[end-1] and multipliers being multipliers[i+1] to multipliers[m-1]. Moreover, the number of operations is lessened by one.

We then have to solve these subproblems successively and at each step return the maximum of two possible answers. When all operations are done, we can return 0.

Approach 1: Brute Force

Intuition

As explained above, at each step we have to reduce the problem to two subproblems, with one less operation. And then we have to repeatedly solve these subproblems. This hints at recursion. Now, for solving each subproblem, we essentially need three things

nums: the remaining integers to be considered

multipliers: the remaining multipliers to be considered

op: number of operations done.

Minute Improvement: We need not pass multipliers. We are only interested in its i$^{th}$ element. And this i is closely related to op. If we have done 0 operations, this implies we have to do the next operation from multipliers[0]. If we have done 1 operation, this implies we have to do the next operation from multipliers[1]. And so on.

Note: There are two possible definitions of op. However, logically they are equivalent.

One is to define op as the number of operations done. If we have done 0 operation, this implies we have to do the next operation from multipliers[0]. If we have done 1 operation, this implies we have to do the next operation from multipliers[1]. And so on. In this case, the terminating condition is op == m, when all operations are done.

Another is to define op as the number of operations remaining, then the indexing of multipliers and base condition will change. If we have m-0 operations remaining, this implies we have to do next operation from multipliers[0]. If we have m-1 operation remaining, this implies we have to do next operation from multipliers[1]. And so on. In this case, terminating condition is op == 0, when no operation is remaining.

We have defined op as the number of operations done.

Algorithm

Define a helper function that takes two arguments nums, and op.

If we are done with all operations, op, return 0.

Otherwise,

3.1 One time multiply multipliers[op] with nums[0]. Now, solve the subproblem with nums[1:] and op+1. Add the result of the subproblem to the product.

3.2 Another time multiply multipliers[op] with nums[-1]. Now, solve the subproblem with nums[:-1] and op+1. Add the result of the subproblem to the product.

Return the maximum of two results.

Call the helper function with nums and op=0 as parameters indicating we have done zero operations so far!

Implementation

Note: It is likely to give TLE since the time complexity is too high.

Note that in python string slicing creates another copy. This consumes a lot of memory. We can reduce it too likewise we removed the passing of the multipliers array.

In themultipliers array, we were interested in the left pointer, we obtained it very easily with the op parameter.

In the nums array, we were interested in both left and right pointers. Thus, instead of passing the entire nums, we can pass these pointers.

Note : It is likely to give TLE because the algorithm is not efficient.

Complexity Analysis

Let $M$ be the size of multipliers, the same as the number of operations.

Time complexity: $O(2^M)$.

This can be calculated using the fact that at every step we are reducing the problem of size $M$ to two subproblems of size $M-1$, for doing so we are doing constant time operations (increasing/decreasing left, right and op, and multiplying with y are constant time operations). Thus, the recurrence relation is $T(M)=2T(M-1)+O(1)$, which can be solved using Master Theorem and the result is $O(2^M)$.

Another way of analyzing is that at each step, we branch two sub-tree. The height of the recursion tree will be $M$. Thus, there will be $2^M$ nodes in the recursion tree.

Space complexity: $O(M)$, the recursion stack will take $O(M)$ space.

Approach 2: Recursive Dynamic Programming

Intuition

We can notice that we may need to solve for a particular (left, right, op) state multiple times.

Example: nums = [a, b, c, d, e] and multipliers = [u, v, w, x, y]

The tree indicates that from two different paths, we have reached a common state/sub-problem x with c vs d. Thus, we have repeated states. Hence, we can solve this once, and can use its result.

Thus, Dynamic Programming. Select best from all possible states and instead of computing again and again, save what you have already computed. Memoizing the pre-visited states while trying all the possible scenarios will reduce the complexity.

To determine a state, we essentially need 3 things

left: specify we have used left integers from the left side of nums so far. Next, we may use nums[left]

right: specify we have used right integers from the right side of nums so far. Next, we may use nums[right]

op: number of operations done.

Hence, there are 3 state variables, left, right, and op. Thus, it's a 3D Dynamic Programming problem. And to memoize it, we may need a 3D array.

If there are n state variables, then we need an array of at most n dimensions.

However, with mathematics, we can reduce these 3 state variables to 2. Can you think of how to do that? Try this multiple choice question!

(Note that len(nums) will be constant in this approach, since we are not modifying nums and passing indices instead)

!mcq!

{

"description": " The right is related to op, left and len(nums) as",

"answer": 1,

"choices": [

{ "description": "len(nums)-1-left" },

{ "description": "len(nums)-1-(op-left)" },

{ "description": "len(nums)-1-op" },

{ "description": "op-(left-len(nums))" }

],

"explanation": "Now, there are len(nums) elements in nums. If we have accomplished op operations, and the left-pointer is ahead by left, means there are op - left operations from the right side. Thus, right should be behind n-1 by op-left. Thus, this formula."

}

!mcq!

Therefore, we can define a state with only two state variables op and left. We will use dp to denote the state in the following.

dp[op][left] stores the maximum possible score after we have done op total operations and used left numbers from the left/start side.

From this state, we have two options

Select Left: Number of operations will advance by one. And, so does the left pointer. Thus, we will multiply mulitpliers[op] and nums[left] (since we have selected from left), and add this product to (optimal) result of state dp[op+1][left+1].

Select Right: Then also the number of operations will advance by one. Then, we will multiply mulitpliers[op] with nums[n-1-(op-left)] (since we have selected from right), and add this product to (optimal) result of state dp[op+1][left] (Now, left will not increment since number has not been chosen from left).

Select maximum of results obtained by selecting from Left, and Right.

If op == m, means we have performed m operations, add 0. The base Condition of Recursion.

Let $\text{mul}$ represent multipliers, and $\text{nums}$ represent nums. Then we can have the following equation!

$$

dp[op][left] =

\begin{cases}

\text{max}\bigg((\text{mul}[op]\cdot \text{nums}[left]) + dp[op+1][left+1],\\quad\quad\quad(\text{mul}[op]\cdot \text{nums}[n-1-(op-left)]) + dp[op+1][left]\bigg), & \text{if $op \neq m$ } \[2ex]

0, & \text{if $op=m$}

\end{cases}

$$

Algorithm

Initialize variable m as the size of multipliers and n as the size of nums. Now, m will help us in determining the number of operations, and with help of n, we can calculate the right pointer.

Create a dictionary memo to memoize states.

Define a dp function that takes two arguments op, and left.

If we are done with all operations, i.e. op == m, return 0.

If we have already computed and memoized the state, return the value from the dictionary.

Otherwise,

6.1. One time multiply multipliers[op] with nums[left]. Now, solve the subproblem with left+1 and op+1 as parameters. Add the result of the subproblem to the product.

6.2. Another time multiply multipliers[op] with nums[(n-1)-(op-left)]. The index points to the right pointer. Now, solve the subproblem with left and op+1 as parameters. Add the result of the subproblem to the product.

Memoize the maximum of 6.1 and 6.2 as the result of state (op, left).

Return the memoized result of state op, left.

Call the dp function with op = 0 and left = 0.

Implementation

Note: The python code of top-down DP may give Time Limit Exceeded/Memory Limit Exceeded since it has a larger constant factor.

Complexity Analysis

Let $M$ be the size of multipliers, the same as the number of operations.

Time complexity: $O(M^2)$.

op can vary from 0 to M-1. Now, in two recursive calls that we are making, one time we are incrementing left, alongwith op. Other time, we are not incrementing left, but incrementing op. So, left is atmost op. Thus, left also varies from 0 to M-1. So, there are $O(M^2)$ such pairs for computing.

Space complexity: $O(M^2)$, the memo will store atmost $M^2$ such pairs!

Approach 3: Iterative Dynamic Programming

Intuition

Using the same equation, we can solve this problem using iterative dynamic programming. We will start from the base condition, and then we will compute the optimal result for each state.

We need to convert function dp(operation, left) to dp[op][left]. Hence, we need to use a 2D array. But, we only need those cells where op >= left. Hence, we only need the bottom-right triangle, where left will always be less than or equal to op. As you can see in the diagram, red cells are invalid.

Since we know the base condition, we can start from op = m and fill it up. Moreover, since the number of operations at any stage is greater than or equal to the integer chosen from left (op >= left), the left will drop from op to 0.

Algorithm

Create a 2D array dp of size m+1 by m+1. Reason being op can vary from 0 to m, and so does left.

Initialized all elements to 0. By this initialization, we have filled the base condition, that when we have done m operations, and this operation will add nothing to the result.

Iterate over op from m-1 to 0.

For each op, iterate over left from op to 0.

Using the equation, compute the optimal result for state dp[op][left].

We have formulated the problem in such a way that dp[0][0] will force us to do m operations in a row. In other words, dp[0][0] stores the optimal answer. Hence, we can return dp[0][0] at the end.

Implementation

Complexity Analysis

Let $M$ be the size of multipliers, the same as the number of operations.

Time complexity: $O(M^2)$

op varies from M-1 to 0, and left varies from to op to 0. This is equivalent to iterating half matrix of order $M\times M$. So, we are computing $O(\frac{M^2}{2})$ states.

Space complexity: $O(M^2)$, as evident from dp array.

Approach 4: Space Optimized Dynamic Programming

Intuition

On carefully eyeing mathematical formula and iterative code, we can say that for computing present row $dp[op]$, we need next row $dp[op+1]$ ONLY. Therefore, we can have 1D Array only. We will fill the dp array from bottom to top, saving the next row in a temporary array, and then computing the present row using the saved temporary array.

Interview Tip: Always spend time on forming and analyzing Dynamic Programming Equation. For Dynamic Programming, forming an equation requires time. Writing code isn't tough in most cases. Moreover, by analyzing the equation, quite a few times, we can solve the problem using less space.

Algorithm

Create a 1D array dp of size m+1. Initialize all elements to 0.

Iterate over op from m-1 to 0.

Since dp array will be modified using next row, and this next row result is saved in dp only, we need to use a temporary array to store the next row. Thus, save all current entries of dp in next_row.

For each op, iterate over left from op to 0.

Using the equation, compute the optimal result for state dp[left].

Return dp[0] at the end.

Implementation

Complexity Analysis

Let $M$ be the size of multipliers, the same as the number of operations.

Time complexity: $O(M^2)$

op varies from M-1 to 0, and left varies from to op to 0. This is equivalent to iterating half matrix of order $M\times M$. So, we are computing $O(\frac{M^2}{2})$ states.

Space complexity: $O(M)$, since we have used dp array of size M.

https://leetcode.com/articles/maximum-sc...perations/

You are given two integer arrays nums and multipliers of size n and m respectively, where n >= m. The arrays are 1-indexed.

You begin with a score of 0. You want to perform exactly m operations. On the ith operation (1-indexed), you will:

Choose one integer x from either the start or the end of the array nums.

Add multipliers[i] * x to your score.

Remove x from the array nums.

Return the maximum score after performing m operations.

Example 1:

Input: nums = [1,2,3], multipliers = [3,2,1]

Output: 14

Explanation: An optimal solution is as follows:

- Choose from the end, [1,2,3], adding 3 * 3 = 9 to the score.

- Choose from the end, [1,2], adding 2 * 2 = 4 to the score.

- Choose from the end, [1], adding 1 * 1 = 1 to the score.

The total score is 9 + 4 + 1 = 14.

Example 2:

Input: nums = [-5,-3,-3,-2,7,1], multipliers = [-10,-5,3,4,6]

Output: 102

Explanation: An optimal solution is as follows:

- Choose from the start, [-5,-3,-3,-2,7,1], adding -5 * -10 = 50 to the score.

- Choose from the start, [-3,-3,-2,7,1], adding -3 * -5 = 15 to the score.

- Choose from the start, [-3,-2,7,1], adding -3 * 3 = -9 to the score.

- Choose from the end, [-2,7,1], adding 1 * 4 = 4 to the score.

- Choose from the end, [-2,7], adding 7 * 6 = 42 to the score.

The total score is 50 + 15 - 9 + 4 + 42 = 102.

Constraints:

n == nums.length

m == multipliers.length

1 <= m <= 103

m <= n <= 105

-1000 <= nums[i], multipliers[i] <= 1000

[TOC]

Solution

Overview

As per the problem description, we have to do m operations and find the maximum score. At every operation, we have to select $i^{th}$ integer from multipliers and multiply it with an integer x from nums. The integer x can be chosen from either the start or the end of the nums. And then we have to remove that integer from nums.

At first glance, a greedy approach looks promising. In step i, out of nums[start] and nums[end], we can pick the integer x that maximizes x * multipliers[i].

This greedy approach works well for one of the given examples.

nums = [1,2,3], multipliers = [3,2,1]

Taking Decision

‣ From multipliers, we have 3, nums is [1, 2, 3], from 3 * 1 and 3 * 3, pick 3, add 3 * 3 = 9.

‣ From multipliers, we have 2, nums is [1, 2], from 2 * 1 and 2 * 2, pick 2, add 2 * 2 = 4.

‣ From multipliers, we have 1, nums is [1], add 1 * 1 = 1.

Total Score is 9+4+1=14, which is correct

However, it fails for the second example.

nums = [-5,-3,-3,-2,7,1], multipliers = [-10,-5,3,4,6]

Taking Decision

‣ From multipliers, we have 10, nums is [-5,-3,-3,-2,7,1], from (-10) * (-5) and (-10) * 1, pick -5, add (-10) * (-5) = 50.

‣ From multipliers, we have -5, nums is [-3,-3,-2,7,1], from (-5) * (-3) and (-5) * 1, pick -3, add (-5) * (-3) = 15.

‣ From multipliers, we have 3, nums is [-3,-2,7,1], from 3 * (-3) and 3 * 1, pick 1, add 3 * 1 = 3.

‣ From multipliers, we have 4, nums is [-3,-2,7], from 4 * (-3) and 4 * 7, pick 7, add 4 * 7 = 28.

‣ From multipliers, we have 6, nums is [-3,-2], from 6 * (-3) and 6 * (-2), pick -2, add 6 * (-2) = -12.

Total Score is 50+15+3+28+(-12)=84 which isn't optimal.

102 is Optimal Solution as given in Problem Example.

The logical intuition of why it is not optimal can be deduced from the following two cases:

Greedy is short-sighted. For global optimum, we pick local optimum. But picking this Local Optimum may restrict greater positive product afterward.

nums = [-10,1,1000,1,1,100], multipliers = [1,1,1]

If we pick 100 over -10, we would never ever be able to collect 1000. There are only three elements in multipliers, and we can collect 1000 by taking the left integers only. But selecting 100 at the first point restricts it.

Moreover, what if both ends of nums are identical? We don't know which one to favor. One may yield another score, another may yield a very different score.

nums = [2, 1000, -1, 2], multipliers = [1, 1]

a. if we select Left 2 first, then at the next step, there would be a contest between Left 1000 and Right 2. As per approach we now would select left 1000, obtaining 1002 as the answer.

b. if we select Right 2 first, then at the next step, there would be a contest between Left 2 and Right -1. As per approach we now would select left 2, obtaining 4 as the answer.

Thus, these examples suggest that we have to look for all two possible combinations at each step:

Select nums[start], now problem reduces to another subproblem with nums being nums[start+1] to nums[end] and multipliers being multipliers[i+1] to multipliers[m-1]. Moreover, the number of operations is lessened by one.

Select nums[end], now problem reduces to another subproblem with nums being nums[start] to nums[end-1] and multipliers being multipliers[i+1] to multipliers[m-1]. Moreover, the number of operations is lessened by one.

We then have to solve these subproblems successively and at each step return the maximum of two possible answers. When all operations are done, we can return 0.

Approach 1: Brute Force

Intuition

As explained above, at each step we have to reduce the problem to two subproblems, with one less operation. And then we have to repeatedly solve these subproblems. This hints at recursion. Now, for solving each subproblem, we essentially need three things

nums: the remaining integers to be considered

multipliers: the remaining multipliers to be considered

op: number of operations done.

Minute Improvement: We need not pass multipliers. We are only interested in its i$^{th}$ element. And this i is closely related to op. If we have done 0 operations, this implies we have to do the next operation from multipliers[0]. If we have done 1 operation, this implies we have to do the next operation from multipliers[1]. And so on.

Note: There are two possible definitions of op. However, logically they are equivalent.

One is to define op as the number of operations done. If we have done 0 operation, this implies we have to do the next operation from multipliers[0]. If we have done 1 operation, this implies we have to do the next operation from multipliers[1]. And so on. In this case, the terminating condition is op == m, when all operations are done.

Another is to define op as the number of operations remaining, then the indexing of multipliers and base condition will change. If we have m-0 operations remaining, this implies we have to do next operation from multipliers[0]. If we have m-1 operation remaining, this implies we have to do next operation from multipliers[1]. And so on. In this case, terminating condition is op == 0, when no operation is remaining.

We have defined op as the number of operations done.

Algorithm

Define a helper function that takes two arguments nums, and op.

If we are done with all operations, op, return 0.

Otherwise,

3.1 One time multiply multipliers[op] with nums[0]. Now, solve the subproblem with nums[1:] and op+1. Add the result of the subproblem to the product.

3.2 Another time multiply multipliers[op] with nums[-1]. Now, solve the subproblem with nums[:-1] and op+1. Add the result of the subproblem to the product.

Return the maximum of two results.

Call the helper function with nums and op=0 as parameters indicating we have done zero operations so far!

Implementation

Note: It is likely to give TLE since the time complexity is too high.

Note that in python string slicing creates another copy. This consumes a lot of memory. We can reduce it too likewise we removed the passing of the multipliers array.

In themultipliers array, we were interested in the left pointer, we obtained it very easily with the op parameter.

In the nums array, we were interested in both left and right pointers. Thus, instead of passing the entire nums, we can pass these pointers.

Note : It is likely to give TLE because the algorithm is not efficient.

Complexity Analysis

Let $M$ be the size of multipliers, the same as the number of operations.

Time complexity: $O(2^M)$.

This can be calculated using the fact that at every step we are reducing the problem of size $M$ to two subproblems of size $M-1$, for doing so we are doing constant time operations (increasing/decreasing left, right and op, and multiplying with y are constant time operations). Thus, the recurrence relation is $T(M)=2T(M-1)+O(1)$, which can be solved using Master Theorem and the result is $O(2^M)$.

Another way of analyzing is that at each step, we branch two sub-tree. The height of the recursion tree will be $M$. Thus, there will be $2^M$ nodes in the recursion tree.

Space complexity: $O(M)$, the recursion stack will take $O(M)$ space.

Approach 2: Recursive Dynamic Programming

Intuition

We can notice that we may need to solve for a particular (left, right, op) state multiple times.

Example: nums = [a, b, c, d, e] and multipliers = [u, v, w, x, y]

The tree indicates that from two different paths, we have reached a common state/sub-problem x with c vs d. Thus, we have repeated states. Hence, we can solve this once, and can use its result.

Thus, Dynamic Programming. Select best from all possible states and instead of computing again and again, save what you have already computed. Memoizing the pre-visited states while trying all the possible scenarios will reduce the complexity.

To determine a state, we essentially need 3 things

left: specify we have used left integers from the left side of nums so far. Next, we may use nums[left]

right: specify we have used right integers from the right side of nums so far. Next, we may use nums[right]

op: number of operations done.

Hence, there are 3 state variables, left, right, and op. Thus, it's a 3D Dynamic Programming problem. And to memoize it, we may need a 3D array.

If there are n state variables, then we need an array of at most n dimensions.

However, with mathematics, we can reduce these 3 state variables to 2. Can you think of how to do that? Try this multiple choice question!

(Note that len(nums) will be constant in this approach, since we are not modifying nums and passing indices instead)

!mcq!

{

"description": " The right is related to op, left and len(nums) as",

"answer": 1,

"choices": [

{ "description": "len(nums)-1-left" },

{ "description": "len(nums)-1-(op-left)" },

{ "description": "len(nums)-1-op" },

{ "description": "op-(left-len(nums))" }

],

"explanation": "Now, there are len(nums) elements in nums. If we have accomplished op operations, and the left-pointer is ahead by left, means there are op - left operations from the right side. Thus, right should be behind n-1 by op-left. Thus, this formula."

}

!mcq!

Therefore, we can define a state with only two state variables op and left. We will use dp to denote the state in the following.

dp[op][left] stores the maximum possible score after we have done op total operations and used left numbers from the left/start side.

From this state, we have two options

Select Left: Number of operations will advance by one. And, so does the left pointer. Thus, we will multiply mulitpliers[op] and nums[left] (since we have selected from left), and add this product to (optimal) result of state dp[op+1][left+1].

Select Right: Then also the number of operations will advance by one. Then, we will multiply mulitpliers[op] with nums[n-1-(op-left)] (since we have selected from right), and add this product to (optimal) result of state dp[op+1][left] (Now, left will not increment since number has not been chosen from left).

Select maximum of results obtained by selecting from Left, and Right.

If op == m, means we have performed m operations, add 0. The base Condition of Recursion.

Let $\text{mul}$ represent multipliers, and $\text{nums}$ represent nums. Then we can have the following equation!

$$

dp[op][left] =

\begin{cases}

\text{max}\bigg((\text{mul}[op]\cdot \text{nums}[left]) + dp[op+1][left+1],\\quad\quad\quad(\text{mul}[op]\cdot \text{nums}[n-1-(op-left)]) + dp[op+1][left]\bigg), & \text{if $op \neq m$ } \[2ex]

0, & \text{if $op=m$}

\end{cases}

$$

Algorithm

Initialize variable m as the size of multipliers and n as the size of nums. Now, m will help us in determining the number of operations, and with help of n, we can calculate the right pointer.

Create a dictionary memo to memoize states.

Define a dp function that takes two arguments op, and left.

If we are done with all operations, i.e. op == m, return 0.

If we have already computed and memoized the state, return the value from the dictionary.

Otherwise,

6.1. One time multiply multipliers[op] with nums[left]. Now, solve the subproblem with left+1 and op+1 as parameters. Add the result of the subproblem to the product.

6.2. Another time multiply multipliers[op] with nums[(n-1)-(op-left)]. The index points to the right pointer. Now, solve the subproblem with left and op+1 as parameters. Add the result of the subproblem to the product.

Memoize the maximum of 6.1 and 6.2 as the result of state (op, left).

Return the memoized result of state op, left.

Call the dp function with op = 0 and left = 0.

Implementation

Note: The python code of top-down DP may give Time Limit Exceeded/Memory Limit Exceeded since it has a larger constant factor.

Complexity Analysis

Let $M$ be the size of multipliers, the same as the number of operations.

Time complexity: $O(M^2)$.

op can vary from 0 to M-1. Now, in two recursive calls that we are making, one time we are incrementing left, alongwith op. Other time, we are not incrementing left, but incrementing op. So, left is atmost op. Thus, left also varies from 0 to M-1. So, there are $O(M^2)$ such pairs for computing.

Space complexity: $O(M^2)$, the memo will store atmost $M^2$ such pairs!

Approach 3: Iterative Dynamic Programming

Intuition

Using the same equation, we can solve this problem using iterative dynamic programming. We will start from the base condition, and then we will compute the optimal result for each state.

We need to convert function dp(operation, left) to dp[op][left]. Hence, we need to use a 2D array. But, we only need those cells where op >= left. Hence, we only need the bottom-right triangle, where left will always be less than or equal to op. As you can see in the diagram, red cells are invalid.

Since we know the base condition, we can start from op = m and fill it up. Moreover, since the number of operations at any stage is greater than or equal to the integer chosen from left (op >= left), the left will drop from op to 0.

Algorithm

Create a 2D array dp of size m+1 by m+1. Reason being op can vary from 0 to m, and so does left.

Initialized all elements to 0. By this initialization, we have filled the base condition, that when we have done m operations, and this operation will add nothing to the result.

Iterate over op from m-1 to 0.

For each op, iterate over left from op to 0.

Using the equation, compute the optimal result for state dp[op][left].

We have formulated the problem in such a way that dp[0][0] will force us to do m operations in a row. In other words, dp[0][0] stores the optimal answer. Hence, we can return dp[0][0] at the end.

Implementation

Complexity Analysis

Let $M$ be the size of multipliers, the same as the number of operations.

Time complexity: $O(M^2)$

op varies from M-1 to 0, and left varies from to op to 0. This is equivalent to iterating half matrix of order $M\times M$. So, we are computing $O(\frac{M^2}{2})$ states.

Space complexity: $O(M^2)$, as evident from dp array.

Approach 4: Space Optimized Dynamic Programming

Intuition

On carefully eyeing mathematical formula and iterative code, we can say that for computing present row $dp[op]$, we need next row $dp[op+1]$ ONLY. Therefore, we can have 1D Array only. We will fill the dp array from bottom to top, saving the next row in a temporary array, and then computing the present row using the saved temporary array.

Interview Tip: Always spend time on forming and analyzing Dynamic Programming Equation. For Dynamic Programming, forming an equation requires time. Writing code isn't tough in most cases. Moreover, by analyzing the equation, quite a few times, we can solve the problem using less space.

Algorithm

Create a 1D array dp of size m+1. Initialize all elements to 0.

Iterate over op from m-1 to 0.

Since dp array will be modified using next row, and this next row result is saved in dp only, we need to use a temporary array to store the next row. Thus, save all current entries of dp in next_row.

For each op, iterate over left from op to 0.

Using the equation, compute the optimal result for state dp[left].

Return dp[0] at the end.

Implementation

Complexity Analysis

Let $M$ be the size of multipliers, the same as the number of operations.

Time complexity: $O(M^2)$

op varies from M-1 to 0, and left varies from to op to 0. This is equivalent to iterating half matrix of order $M\times M$. So, we are computing $O(\frac{M^2}{2})$ states.

Space complexity: $O(M)$, since we have used dp array of size M.

https://leetcode.com/articles/maximum-sc...perations/