Subset Sum
A medium Dynamic Programming problem included in Love Babbar 450, Striver A2Z. Below: the roles whose interviews prioritise this topic, and how to practise it.
- Topic
- Dynamic Programming
- Sheets
- 2
- Core for
- 9 roles
- Platform
- GeeksforGeeks
The problem
Given an array of non-negative integers and a target sum, determine if there exists a subset whose elements sum to exactly the target.
Example 1
- Input
- nums = [3,34,4,12,5,2], target = 9
- Output
- true
- Why
- The subset [4,5] sums to 9.
Example 2
- Input
- nums = [3,34,4,12,5,2], target = 30
- Output
- false
- Why
- No subset of the given numbers sums to 30.
Example 3
- Input
- nums = [1], target = 1
- Output
- true
- Why
- The single element 1 equals the target.
Constraints
- 1 <= nums.length <= 200
- 1 <= nums[i] <= 100
- 1 <= target <= 10^5
How to think about it
Updated 2026-09-09A subset sum of s is achievable if and only if s - x was achievable before considering number x. A boolean bitset or array tracking reachable sums can be updated in reverse to prevent using each element more than once.
Approaches, worst first
Recursive inclusion
time O(2^n) · space O(n)
Branch on taking or skipping each element. Without memoization, branches multiply to 2^n, failing on arrays with up to 200 elements.
2D dynamic programming grid
time O(n * target) · space O(n * target)
dp[i][j] is true if a subset of the first i elements sums to j. Fill row by row: dp[i][j] = dp[i-1][j] || (j >= nums[i-1] ? dp[i-1][j - nums[i-1]] : false).
1D backwards boolean arrayWrite this one
time O(n * target) · space O(target)
Maintain boolean dp of size target + 1 with dp[0] = true. For each num in nums, loop j from target down to num: dp[j] = dp[j] || dp[j - num]. Stop early if dp[target] is true.
Where people lose marks · 3
- Iterating forward from num to target in 1D array. This simulates an infinite supply of each number.
- Missing the base case dp[0] = true. The empty subset always produces a sum of 0.
- Allocating memory when target is vastly larger than total array sum. If target > sum(nums), return false immediately before allocating tables.
The theory behind it
Dynamic Programming — the ground this problem stands on. All Dynamic Programming problems
What Dynamic Programming is
Dynamic programming is a method for solving a complex problem by breaking it into overlapping subproblems, solving each subproblem only once, and remembering the answers in a lookup table. Instead of recalculating identical questions over and over, future steps look up previous answers directly. By assembling these saved pieces from the bottom up or storing them during recursion, a task that would take billions of steps finishes in a fraction of a second.
When to reach for it
Reach for dynamic programming when questions ask for the maximum profit, minimum cost, total number of distinct ways to achieve a goal, or whether a target can be formed. Signals include overlapping choices where making a choice now affects what choices remain later, but greedy picking fails to find the true global optimum. If drawing a recursive decision tree reveals the same subproblem states repeating across branches, dynamic programming is needed.
How the pattern works
Identify the state variables that uniquely describe a subproblem, such as an array index and remaining capacity. Write the base cases first, representing states whose answers are known without calculation. Next, write the recurrence relation that expresses the current state using previously solved states, taking the minimum, maximum, or sum among your options. Build the solution either top-down by caching recursive returns in a memo table, or bottom-up by filling an array in topological dependency order. When each state depends only on the previous row, compress storage down to a single array.
What each operation costs
| Operation | Time |
|---|---|
| fill dynamic programming table of n states | O(n) |
| solve two-dimensional grid of m by n states | O(m * n) |
| space-optimized state transition keeping one row | O(n) |
What usually goes wrong with Dynamic Programming
- Filling a bottom-up table in an order where the current cell needs values that have not been computed yet, reading uninitialized zeros.
- Failing to initialize base cases properly, such as filling a minimization table with zeros instead of infinity, which traps the answer at zero.
- Overwriting values in a 1D space-optimized knapsack array by scanning in the wrong direction, allowing the same item to be chosen multiple times.
Which roles need this problem
Dynamic Programming is a core topic for these 9 roles — if you're targeting one of them, this problem is early in your path, not optional.
Secondary for 5 more roles, including Game Developer, Cryptography Engineer, Performance Engineer.
Companies that have asked it
Tags taken from the problem's own GeeksforGeeks page — not a copied list.
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