hello-algo/en/docs/chapter_dynamic_programming/unbounded_knapsack_problem.md

Unbounded knapsack problem

In this section, we first solve another common knapsack problem: the unbounded knapsack, and then explore a special case of it: the coin change problem.

Unbounded knapsack problem

!!! question

Given $n$ items, where the weight of the $i^{th}$ item is $wgt[i-1]$ and its value is $val[i-1]$, and a backpack with a capacity of $cap$. **Each item can be selected multiple times**. What is the maximum value of the items that can be put into the backpack without exceeding its capacity? See the example below.

Dynamic programming approach

The unbounded knapsack problem is very similar to the 0-1 knapsack problem, the only difference being that there is no limit on the number of times an item can be chosen.

• In the 0-1 knapsack problem, there is only one of each item, so after placing item i into the backpack, you can only choose from the previous i-1 items.
• In the unbounded knapsack problem, the quantity of each item is unlimited, so after placing item i in the backpack, you can still choose from the previous i items.

Under the rules of the unbounded knapsack problem, the state [i, c] can change in two ways.

• Not putting item i in: As with the 0-1 knapsack problem, transition to [i-1, c].
• Putting item i in: Unlike the 0-1 knapsack problem, transition to [i, c-wgt[i-1]].

The state transition equation thus becomes:

dp[i, c] = \max(dp[i-1, c], dp[i, c - wgt[i-1]] + val[i-1])

Code implementation

Comparing the code for the two problems, the state transition changes from i-1 to i, the rest is completely identical:

[file]{unbounded_knapsack}-[class]{}-[func]{unbounded_knapsack_dp}

Space optimization

Since the current state comes from the state to the left and above, the space-optimized solution should perform a forward traversal for each row in the dp table.

This traversal order is the opposite of that for the 0-1 knapsack. Please refer to the figure below to understand the difference.

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The code implementation is quite simple, just remove the first dimension of the array dp:

[file]{unbounded_knapsack}-[class]{}-[func]{unbounded_knapsack_dp_comp}

Coin change problem

The knapsack problem is a representative of a large class of dynamic programming problems and has many variants, such as the coin change problem.

!!! question

Given $n$ types of coins, the denomination of the $i^{th}$ type of coin is $coins[i - 1]$, and the target amount is $amt$. **Each type of coin can be selected multiple times**. What is the minimum number of coins needed to make up the target amount? If it is impossible to make up the target amount, return $-1$. See the example below.

Dynamic programming approach

The coin change can be seen as a special case of the unbounded knapsack problem, sharing the following similarities and differences.

• The two problems can be converted into each other: "item" corresponds to "coin", "item weight" corresponds to "coin denomination", and "backpack capacity" corresponds to "target amount".
• The optimization goals are opposite: the unbounded knapsack problem aims to maximize the value of items, while the coin change problem aims to minimize the number of coins.
• The unbounded knapsack problem seeks solutions "not exceeding" the backpack capacity, while the coin change seeks solutions that "exactly" make up the target amount.

First step: Think through each round's decision-making, define the state, and thus derive the dp table

The state [i, a] corresponds to the sub-problem: the minimum number of coins that can make up the amount a using the first i types of coins, denoted as dp[i, a].

The two-dimensional dp table is of size (n+1) \times (amt+1).

Second step: Identify the optimal substructure and derive the state transition equation

This problem differs from the unbounded knapsack problem in two aspects of the state transition equation.

• This problem seeks the minimum, so the operator \max() needs to be changed to \min().
• The optimization is focused on the number of coins, so simply add +1 when a coin is chosen.

dp[i, a] = \min(dp[i-1, a], dp[i, a - coins[i-1]] + 1)

Third step: Define boundary conditions and state transition order

When the target amount is 0, the minimum number of coins needed to make it up is 0, so all dp[i, 0] in the first column are 0.

When there are no coins, it is impossible to make up any amount >0, which is an invalid solution. To allow the \min() function in the state transition equation to recognize and filter out invalid solutions, consider using +\infty to represent them, i.e., set all dp[0, a] in the first row to +\infty.

Code implementation

Most programming languages do not provide a +\infty variable, only the maximum value of an integer int can be used as a substitute. This can lead to overflow: the +1 operation in the state transition equation may overflow.

For this reason, we use the number amt + 1 to represent an invalid solution, because the maximum number of coins needed to make up amt is at most amt. Before returning the result, check if dp[n, amt] equals amt + 1, and if so, return -1, indicating that the target amount cannot be made up. The code is as follows:

[file]{coin_change}-[class]{}-[func]{coin_change_dp}

The figure below show the dynamic programming process for the coin change problem, which is very similar to the unbounded knapsack problem.

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Space optimization

The space optimization for the coin change problem is handled in the same way as for the unbounded knapsack problem:

[file]{coin_change}-[class]{}-[func]{coin_change_dp_comp}

Coin change problem II

!!! question

Given $n$ types of coins, where the denomination of the $i^{th}$ type of coin is $coins[i - 1]$, and the target amount is $amt$. Each type of coin can be selected multiple times, **ask how many combinations of coins can make up the target amount**. See the example below.

Dynamic programming approach

Compared to the previous problem, the goal of this problem is to determine the number of combinations, so the sub-problem becomes: the number of combinations that can make up amount a using the first i types of coins. The dp table remains a two-dimensional matrix of size (n+1) \times (amt + 1).

The number of combinations for the current state is the sum of the combinations from not selecting the current coin and selecting the current coin. The state transition equation is:

dp[i, a] = dp[i-1, a] + dp[i, a - coins[i-1]]

When the target amount is 0, no coins are needed to make up the target amount, so all dp[i, 0] in the first column should be initialized to 1. When there are no coins, it is impossible to make up any amount >0, so all dp[0, a] in the first row should be set to 0.

Code implementation

[file]{coin_change_ii}-[class]{}-[func]{coin_change_ii_dp}

Space optimization

The space optimization approach is the same, just remove the coin dimension:

[file]{coin_change_ii}-[class]{}-[func]{coin_change_ii_dp_comp}