LeetCode #1986 — MEDIUM

Minimum Number of Work Sessions to Finish the Tasks

Move from brute-force thinking to an efficient approach using array strategy.

Solve on LeetCode
The Problem

Problem Statement

There are n tasks assigned to you. The task times are represented as an integer array tasks of length n, where the ith task takes tasks[i] hours to finish. A work session is when you work for at most sessionTime consecutive hours and then take a break.

You should finish the given tasks in a way that satisfies the following conditions:

  • If you start a task in a work session, you must complete it in the same work session.
  • You can start a new task immediately after finishing the previous one.
  • You may complete the tasks in any order.

Given tasks and sessionTime, return the minimum number of work sessions needed to finish all the tasks following the conditions above.

The tests are generated such that sessionTime is greater than or equal to the maximum element in tasks[i].

Example 1:

Input: tasks = [1,2,3], sessionTime = 3
Output: 2
Explanation: You can finish the tasks in two work sessions.
- First work session: finish the first and the second tasks in 1 + 2 = 3 hours.
- Second work session: finish the third task in 3 hours.

Example 2:

Input: tasks = [3,1,3,1,1], sessionTime = 8
Output: 2
Explanation: You can finish the tasks in two work sessions.
- First work session: finish all the tasks except the last one in 3 + 1 + 3 + 1 = 8 hours.
- Second work session: finish the last task in 1 hour.

Example 3:

Input: tasks = [1,2,3,4,5], sessionTime = 15
Output: 1
Explanation: You can finish all the tasks in one work session.

Constraints:

  • n == tasks.length
  • 1 <= n <= 14
  • 1 <= tasks[i] <= 10
  • max(tasks[i]) <= sessionTime <= 15
Patterns Used

Roadmap

  1. Brute Force Baseline
  2. Core Insight
  3. Algorithm Walkthrough
  4. Edge Cases
  5. Full Annotated Code
  6. Interactive Study Demo
  7. Complexity Analysis
Step 01

Brute Force Baseline

Problem summary: There are n tasks assigned to you. The task times are represented as an integer array tasks of length n, where the ith task takes tasks[i] hours to finish. A work session is when you work for at most sessionTime consecutive hours and then take a break. You should finish the given tasks in a way that satisfies the following conditions: If you start a task in a work session, you must complete it in the same work session. You can start a new task immediately after finishing the previous one. You may complete the tasks in any order. Given tasks and sessionTime, return the minimum number of work sessions needed to finish all the tasks following the conditions above. The tests are generated such that sessionTime is greater than or equal to the maximum element in tasks[i].

Baseline thinking

Start with the most direct exhaustive search. That gives a correctness anchor before optimizing.

Pattern signal: Array · Dynamic Programming · Backtracking · Bit Manipulation

Example 1

[1,2,3]
3

Example 2

[3,1,3,1,1]
8

Example 3

[1,2,3,4,5]
15

Related Problems

  • Smallest Sufficient Team (smallest-sufficient-team)
  • Fair Distribution of Cookies (fair-distribution-of-cookies)
  • Find Minimum Time to Finish All Jobs (find-minimum-time-to-finish-all-jobs)
  • Find Minimum Time to Finish All Jobs II (find-minimum-time-to-finish-all-jobs-ii)
Step 02

Core Insight

What unlocks the optimal approach

  • Try all possible ways of assignment.
  • If we can store the assignments in form of a state then we can reuse that state and solve the problem in a faster way.
Interview move: turn each hint into an invariant you can check after every iteration/recursion step.
Step 03

Algorithm Walkthrough

Iteration Checklist

  1. Define state (indices, window, stack, map, DP cell, or recursion frame).
  2. Apply one transition step and update the invariant.
  3. Record answer candidate when condition is met.
  4. Continue until all input is consumed.
Use the first example testcase as your mental trace to verify each transition.
Step 04

Edge Cases

Minimum Input
Single element / shortest valid input
Validate boundary behavior before entering the main loop or recursion.
Duplicates & Repeats
Repeated values / repeated states
Decide whether duplicates should be merged, skipped, or counted explicitly.
Extreme Constraints
Upper-end input sizes
Re-check complexity target against constraints to avoid time-limit issues.
Invalid / Corner Shape
Empty collections, zeros, or disconnected structures
Handle special-case structure before the core algorithm path.
Step 05

Full Annotated Code

Source-backed implementations are provided below for direct study and interview prep.

// Accepted solution for LeetCode #1986: Minimum Number of Work Sessions to Finish the Tasks
class Solution {
    public int minSessions(int[] tasks, int sessionTime) {
        int n = tasks.length;
        boolean[] ok = new boolean[1 << n];
        for (int i = 1; i < 1 << n; ++i) {
            int t = 0;
            for (int j = 0; j < n; ++j) {
                if ((i >> j & 1) == 1) {
                    t += tasks[j];
                }
            }
            ok[i] = t <= sessionTime;
        }
        int[] f = new int[1 << n];
        Arrays.fill(f, 1 << 30);
        f[0] = 0;
        for (int i = 1; i < 1 << n; ++i) {
            for (int j = i; j > 0; j = (j - 1) & i) {
                if (ok[j]) {
                    f[i] = Math.min(f[i], f[i ^ j] + 1);
                }
            }
        }
        return f[(1 << n) - 1];
    }
}
Step 06

Interactive Study Demo

Use this to step through a reusable interview workflow for this problem.

Press Step or Run All to begin.
Step 07

Complexity Analysis

Time
O(n × 3^n)
Space
O(2^n)

Approach Breakdown

RECURSIVE
O(2ⁿ) time
O(n) space

Pure recursion explores every possible choice at each step. With two choices per state (take or skip), the decision tree has 2ⁿ leaves. The recursion stack uses O(n) space. Many subproblems are recomputed exponentially many times.

DYNAMIC PROGRAMMING
O(n × m) time
O(n × m) space

Each cell in the DP table is computed exactly once from previously solved subproblems. The table dimensions determine both time and space. Look for the state variables — each unique combination of state values is one cell. Often a rolling array can reduce space by one dimension.

Shortcut: Count your DP state dimensions → that’s your time. Can you drop one? That’s your space optimization.
Coach Notes

Common Mistakes

Review these before coding to avoid predictable interview regressions.

Off-by-one on range boundaries

Wrong move: Loop endpoints miss first/last candidate.

Usually fails on: Fails on minimal arrays and exact-boundary answers.

Fix: Re-derive loops from inclusive/exclusive ranges before coding.

State misses one required dimension

Wrong move: An incomplete state merges distinct subproblems and caches incorrect answers.

Usually fails on: Correctness breaks on cases that differ only in hidden state.

Fix: Define state so each unique subproblem maps to one DP cell.

Missing undo step on backtrack

Wrong move: Mutable state leaks between branches.

Usually fails on: Later branches inherit selections from earlier branches.

Fix: Always revert state changes immediately after recursive call.