
Problem Statement
In the described problem, you are provided with an array of integers, named nums
, and a target integer value. Your task is to construct mathematical expressions using all the integers in the nums
array. Each integer in the array can be prefixed with either a plus ('+'
) or minus ('-'
) symbol. These prefixed integers are then concatenated to form an expression. The objective is to count how many different expressions can be formed whose resultant value after evaluation equals the provided target
value. For instance, with nums = [2, 1]
, prefixing 2
with '+'
and 1
with '-'
results in the expression "+2-1"
. The challenge is to explore all possible combinations of the '+'
and '-'
prefixes for the numbers in nums
to see which combinations sum up to the target
.
Examples
Example 1
Input:
nums = [1,1,1,1,1], target = 3
Output:
5
Explanation:
There are 5 ways to assign symbols to make the sum of nums be target 3. -1 + 1 + 1 + 1 + 1 = 3 +1 - 1 + 1 + 1 + 1 = 3 +1 + 1 - 1 + 1 + 1 = 3 +1 + 1 + 1 - 1 + 1 = 3 +1 + 1 + 1 + 1 - 1 = 3
Example 2
Input:
nums = [1], target = 1
Output:
1
Constraints
1 <= nums.length <= 20
0 <= nums[i] <= 1000
0 <= sum(nums[i]) <= 1000
-1000 <= target <= 1000
Approach and Intuition
The problem presents a classic case for employing a depth-first search (DFS) or dynamic programming approach, particularly suitable given the constraints. Here is the logical breakdown and intuition behind approaching this problem:
Recursive DFS Approach:
- At each number in the
nums
array, choose to either add or subtract the number. - Recursively proceed to the next number with the updated current sum.
- If you reach the end of the array, check if the current sum equals the target. If yes, it's a valid expression.
- Count all such valid expressions.
- At each number in the
Dynamic Programming Approach (Using Subset Sum Concept):
- This approach involves using an array or hashmap to track ways to achieve possible sums.
- Initially, only zero sum is achievable (with zero numbers used).
- For each number in
nums
, update the possible sums. Each number can potentially turn an already possible sum into a new sum by either adding or subtracting the current number. - By the end of this accumulation, check how many ways there are to achieve the
target
sum.
By understanding the constraints:
- The maximum length of
nums
is 20, and each element is between 0 and 1000 while the total sum doesn't exceed 1000 and target ranges between -1000 to 1000, it becomes evident that while a brute-force approach could technically work (due to relatively smaller size), optimizations using dynamic programming can significantly reduce computational overhead and improve efficiency, particularly when dealing with larger arrays or more extreme target values.
Solutions
- C++
- Java
- Python
class Solution {
public:
int calculateWays(vector<int>& elements, int goal) {
int sumAll = accumulate(elements.begin(), elements.end(), 0);
vector<int> ways(2 * sumAll + 1, 0);
ways[elements[0] + sumAll] = 1; // Positive case for the first element
ways[-elements[0] + sumAll] += 1; // Negative case for the first element
for (int i = 1; i < elements.size(); i++) {
vector<int> currentWays(2 * sumAll + 1, 0);
for (int s = -sumAll; s <= sumAll; s++) {
if (ways[s + sumAll] > 0) {
currentWays[s + elements[i] + sumAll] += ways[s + sumAll];
currentWays[s - elements[i] + sumAll] += ways[s + sumAll];
}
}
ways = currentWays;
}
return abs(goal) > sumAll ? 0 : ways[goal + sumAll];
}
};
This explanation walks through a C++ implementation designed to solve the "Target Sum" problem using a dynamic programming approach. Implementing this will allow for the determination of the number of ways to assign a "+" or "-" sign to elements of a given array to achieve a specific target sum.
Begin by calculating the total sum of the elements in the input vector. This sum determines the range of possible sums achievable using the elements.
Initialize a vector,
ways
, sized to2 * sumAll + 1
to account for every possible combination of sums from-sumAll
tosumAll
. Initialize it with zeros.For the first element of the array, set up base cases by marking the positive and the negative of the first element in the
ways
vector.Iterate through each element starting from the second one. For each element:
Create a new temporary vector,
currentWays
, identical in size toways
, initialized to zero.For each possible sum
s
from-sumAll
tosumAll
, where there were previously recorded ways to achieves
, updatecurrentWays
for the indices corresponding to adding and subtracting the current element tos
.Assign
currentWays
back toways
to update the state for the next iteration.
At the end of the iterations, check if the absolute value of the target sum exceeds
sumAll
. If it does, return 0 because it's impossible to reach a sum beyond the total possible sum. Otherwise, return the value at the index corresponding to the target sum adjusted bysumAll
in theways
vector, which indicates the count of ways to achieve that sum.
public class Solution {
public int countTargetWays(int[] elements, int goal) {
int cumulativeSum = Arrays.stream(elements).sum();
int[] states = new int[2 * cumulativeSum + 1];
// Initialize the base case for DP array
states[elements[0] + cumulativeSum] = 1; // Initial positive case
states[-elements[0] + cumulativeSum] += 1; // Initial negative case
// Populate the DP array
for (int i = 1; i < elements.length; i++) {
int[] newStates = new int[2 * cumulativeSum + 1];
for (int s = -cumulativeSum; s <= cumulativeSum; s++) {
if (states[s + cumulativeSum] > 0) {
newStates[s + elements[i] + cumulativeSum] += states[s + cumulativeSum];
newStates[s - elements[i] + cumulativeSum] += states[s + cumulativeSum];
}
}
states = newStates;
}
// Output the result
return Math.abs(goal) > cumulativeSum ? 0 : states[goal + cumulativeSum];
}
}
The provided Java code solves the "Target Sum" problem by using a dynamic programming approach to determine how many different ways there are to add and subtract the given numbers in the array (elements
) to reach the given goal
value. The implemented function countTargetWays
returns the count of such ways.
Overview:
- The function begins by calculating the cumulative sum of all elements in the array.
- A dynamic programming array
states
is initialized. Its size is[2 * cumulativeSum + 1]
to accommodate possible negative indices. - The array is then prepared by setting up initial conditions based on the first element of the
elements
array. - Iterating through the
elements
, the function updates thestates
array for each element to keep track of all possible sums up to that point. - The possible sums are continuously updated by adding and subtracting the current element to all previously computed sums.
- The result for the target
goal
is determined by checking the desired final position in thestates
array, taking into account the offset caused by having included the array to manage negative values.
Key Details:
- The array
states
uses an offset ofcumulativeSum
to handle negative indices, allowing subtraction operations that result in negative values by mapping these values to non-negative array indices. - If the absolute value of the goal exceeds the cumulative sum, it's not possible to reach the goal, hence the function directly returns 0.
- The complexity of the solution considers both the number of elements and the range of potential sums (proportional to the cumulative sum), resulting in a time complexity of O(n*sum) where n is the number of elements and sum is the cumulative sum.
Ensure that all array indices are accessed safely by offsetting with cumulativeSum
and that the input array is non-empty to avoid possible errors during access. Moreover, consider edge cases such as elements with zero, highly positive, or negative goal values relative to the sum of the elements.
class Solution:
def countSubsetsWithTargetSum(self, elements: List[int], required_sum: int) -> int:
total = sum(elements)
ways = [0] * (2 * total + 1)
# Initialize for the first element
ways[elements[0] + total] = 1
ways[-elements[0] + total] += 1
# Compute the ways for all elements
for i in range(1, len(elements)):
next_ways = [0] * (2 * total + 1)
for existing_sum in range(-total, total + 1):
if ways[existing_sum + total] > 0:
next_ways[existing_sum + elements[i] + total] += ways[existing_sum + total]
next_ways[existing_sum - elements[i] + total] += ways[existing_sum + total]
ways = next_ways
# Final result calculation
return 0 if abs(required_sum) > total else ways[required_sum + total]
Implement the "Target Sum" solution in Python3 which calculates the number of ways subsets of given integers can be arranged to sum up to a specified target. This solution employs dynamic programming, specifically using a 1D array to store intermediate results for optimization.
Understand that the function
countSubsetsWithTargetSum
accepts two arguments:elements
, a list of integers which are the sets of numbers available.required_sum
, the integer target sum to achieve.
The code initially calculates the summed total of the input list
elements
. This total is used to define the size of the 1D arrayways
which helps in mapping sum combinations relative to zero (adjusted bytotal
).Begin by initializing
ways
to account for positive and negative occurrences of the first element inelements
. This considers both adding and subtracting the first value from the total sum.Progress through each element in the
elements
list:- For each index, start a new array
next_ways
to store combinations up to that point. - Check the current sums (
existing_sum
) inways
that are non-zero. - Update
next_ways
by adding and subtracting the current element (elements[i]
) to the indices determined byexisting_sum
.
- For each index, start a new array
Calculate the final result:
- If the absolute value of
required_sum
exceedstotal
, it's impossible to reach the target with the given integers, hence return 0. - Otherwise, return the value at the index adjusted by
required_sum
in theways
array, which will provide the count of subsets that add up to therequired_sum
.
- If the absolute value of
By following these strategies and maintaining a space and time efficient approach, this Python3 solution effectively solves the subset sum problem for a target value using dynamic programming techniques.
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