feat: Add Weighted Job Scheduling algorithm to dynamic programming#13378
feat: Add Weighted Job Scheduling algorithm to dynamic programming#13378pkprajapati7402 wants to merge 2 commits intoTheAlgorithms:masterfrom
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- Implemented efficient Weighted Job Scheduling algorithm using Dynamic Programming and Binary Search - Time Complexity: O(n log n), Space Complexity: O(n) - Added comprehensive documentation and example usage - Includes proper author attribution and date - Fixed linting issues: removed unused imports and applied formatting Hacktoberfest contribution - Dynamic Programming implementation for finding maximum profit from non-overlapping job intervals
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| def find_last_non_conflicting(jobs, index): |
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As there is no test file in this pull request nor any test function or class in the file dynamic_programming/weighted_job_scheduling.py, please provide doctest for the function find_last_non_conflicting
Please provide return type hint for the function: find_last_non_conflicting. If the function does not return a value, please provide the type hint as: def function() -> None:
Please provide type hint for the parameter: jobs
Please provide type hint for the parameter: index
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| def weighted_job_scheduling(jobs): |
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As there is no test file in this pull request nor any test function or class in the file dynamic_programming/weighted_job_scheduling.py, please provide doctest for the function weighted_job_scheduling
Please provide return type hint for the function: weighted_job_scheduling. If the function does not return a value, please provide the type hint as: def function() -> None:
Please provide type hint for the parameter: jobs
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| # Step 1: Sort jobs by their finish time | ||
| jobs.sort(key=lambda x: x[1]) |
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Please provide descriptive name for the parameter: x
- Fixed remaining line length issues in docstrings - All lines now comply with 88 character limit - Removed trailing whitespace - Addresses algorithms-keeper bot formatting requirements
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Hacktoberfest contribution - Dynamic Programming implementation for finding maximum profit from non-overlapping job intervals
Describe your change:
This PR adds a new Weighted Job Scheduling algorithm to the dynamic programming section. The algorithm solves the classic problem of selecting a subset of jobs with maximum total profit where no two jobs overlap in time. The solution uses dynamic programming combined with binary search for optimal efficiency.
Key Features:
Algorithm Details:
Checklist: