High-speed analytical C++ engine to solve the subset sum problem with exact cardinality constraint (k-Subset Sum). Instantly processes massive instances with giant integers of up to 1000 bits in a constant time of 3 seconds with absolute zero error. Perfect for logistics payload optimization, indexed portfolio balancing, and manufacturing stock-cutting waste reduction.
{"less":{"a":["8","50"],"e":0,"error":"0","sum":"58"},"greater":{"a":["8","50"],"e":0,"error":"0","sum":"58"},"execution_miliseconds":0,"status":"success"}
curl --location --request POST 'https://zylalabs.com/api/13201/k-subset+sum+optimization+api/26939/find+the+elements+with+sum+closest+to+a+target' --header 'Authorization: Bearer YOUR_API_KEY'
--data-raw '{
"a": [ "1", "2", "4", "8", "50" ],
"target": "58",
"nelem": 2,
"timeoutinmiliseconds": 3000
}'
After signing up, every developer is assigned a personal API access key, a unique combination of letters and digits provided to access to our API endpoint. To authenticate with the k-Subset Sum Optimization API simply include your bearer token in the Authorization header.
| Header | Description |
|---|---|
Authorization
|
Required
Should be Bearer access_key. See "Your API Access Key" above when you are subscribed.
|
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The API returns a JSON object containing two optimal subsets: "less" and "greater." Each subset includes an array of strings representing the elements that sum closest to, but less than or equal to, and greater than or equal to the target, respectively.
The key fields in the response include "less" and "greater," each containing an array "a" of selected elements, "e" for the number of elements, "error" for any processing errors, and "sum" for the total of the subset. Additionally, "execution_miliseconds" indicates the processing time.
The response data is structured as a JSON object with two main sections: "less" and "greater." Each section contains the subset of elements, their count, any errors encountered, the sum of the elements, and the execution time, allowing for easy parsing and utilization.
The endpoint accepts four parameters: "a" (array of strings for input numbers), "target" (string for the target sum), "nelem" (integer for the required number of elements), and "timeoutinmiliseconds" (integer for execution time limit). Each parameter customizes the request for specific needs.
Users can customize their requests by adjusting the "target" value to specify the desired sum, setting "nelem" to define the exact number of elements in the subset, and using "timeoutinmiliseconds" to control the maximum execution time, ensuring tailored results.
Typical use cases include logistics for payload balancing, finance for portfolio rebalancing, and manufacturing for optimizing cutting stock to reduce waste. The API efficiently handles large datasets, making it suitable for complex optimization problems in these domains.
Data accuracy is maintained through the use of arbitrary precision for integer inputs, ensuring no overflow occurs. The C++ engine employs advanced pruning algorithms and parallel processing, which enhances the reliability of the results returned by the API.
If the API cannot find a valid subset that meets the criteria, it will return the best solutions discovered up to the timeout. Users should check the "error" field in the response to understand any issues and utilize the "less" and "greater" subsets for the closest matches available.
The API can process both positive and negative integers of arbitrary precision, allowing for inputs with 10, 100, or even 1000+ digits. This capability ensures that users can work with very large numbers without encountering overflow issues.
If the execution time exceeds the specified "timeoutinmiliseconds," the API will halt processing and return the best solutions found up to that point. Users can check the "error" field to understand if a timeout occurred and utilize the available "less" and "greater" subsets.
The API operates exclusively on integers. To use decimal numbers, users must scale their inputs by a power of 10 to convert them into whole numbers before submission, ensuring compatibility with the API's requirements.
The "nelem" parameter specifies the exact number of elements required in the resulting subset. This allows users to tailor their requests to meet specific cardinality constraints, ensuring the output aligns with their optimization needs.
The API utilizes a high-speed C++ engine that employs advanced pruning algorithms and multi-threaded execution. This design enables it to process large datasets quickly, achieving results in a constant time of approximately 3 seconds.
Users should check the "error" field in the response to identify any issues encountered during processing. This field provides insights into what went wrong, allowing users to adjust their input parameters or data accordingly.
The "execution_miliseconds" field indicates the time taken by the API to process the request. This metric helps users assess performance and efficiency, especially when working with large datasets or tight time constraints.
The "less" subset provides elements that sum closest to but do not exceed the target, useful for scenarios like budget constraints. The "greater" subset offers elements that meet or exceed the target, ideal for maximizing resource allocation in logistics and finance.
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