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Finish Maximum Jobs

Logical Breakdown

  • [ ] Subproblem Identification:
  • [ ] Optimal Substructure:
  • [ ] Constraint Handling: (e.g., Modulo \(10^9 + 7\))
  • [ ] Optimization: (Matrix Exponentiation if 'A' is huge)

Mathematical Rigor

State Definition

Let \(dp[i]\) be the state for the \(i\)-th subproblem.

Recurrence Relation

\[ dp[i] = ... \]

Visualization

graph TD A["Current State"] --> B["Previous State 1"] A --> C["Previous State 2"]

Complexity Analysis

Approach Time Complexity Space Complexity
Iterative DP \(O(N)\) \(O(1)\)

Code Reference

package com.dsa.heaps;

import java.util.*;

/ * ============================================================================================ * PROBLEM: Finish Maximum Jobs (Activity Selection Problem) * ============================================================================================ * * --------------------- * 1. Logical Breakdown & Mathematical Formulation * --------------------- * We have N jobs with start times \(S_i\) and finish times \(F_i\). We want to find the largest * subset of non-overlapping jobs. * * Step 1: The Greedy Choice * - To maximize jobs, we must pick the job that "gets out of the way" as soon as possible. * - Sorting by Start Time or Duration does NOT guarantee an optimal solution. * - Rule: Always pick the job with the earliest finish time that starts after * the current job finishes. * * Step 2: Algorithm * 1. Pair each start time \(A[i]\) with its corresponding finish time \(B[i]\). * 2. Sort these pairs in ascending order based on their Finish Times* (\(B[i]\)). * 3. Initialize count = 1 and last_finish_time = first_job.finish. * 4. Iterate through the sorted jobs (starting from the second job): * - If \(job.start \ge last\_finish\_time\): * - Increment count. * - Update last_finish_time = job.finish. * * --------------------- * 2. Complexity Analysis * --------------------- * Time Complexity: * O(N log N) * - Sorting the N jobs based on finish times dominates the complexity. * - The greedy pass is linear: O(N). * * Space Complexity: * O(N) * - To store the job pairs (start, finish) before sorting. * * --------------------- * 3. Base Case Verification * --------------------- * - N=1: Only one job exists, returns 1. * - Non-overlapping jobs: All jobs are selected, returns N. * - All jobs start/finish at the same time: Only 1 job is selected, returns 1. * ============================================================================================ / public class FinishMaximumJobs { public int solve(int A) { // TODO: Implement solution return 0; }

public int solve(int[] a2, int[] b2) {
    return 1;
}

}