Method for solving MCKP problem and program product

By converting the lending risk control model into MCKP form, combining dominance theory and greedy algorithm optimization selection strategy, combined with integer planning and vectorization operations, the problem of high difficulty and low efficiency in solving the MCKP problem in the lending risk control field is solved, and fast and accurate lending combination selection is achieved.

CN120235690APending Publication Date: 2025-07-01RUIZHI HECHUANG (BEIJING) TECH CO LTD
0 Cites 0 Cited by

Patent Information

Application Number
CN202510098309.7
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-01-22
Publication Date
2025-07-01

AI Technical Summary

Technical Problem

When the prior art deals with the multi-choice backpack problem (MCKP), especially in the field of lending risk control, there are problems such as high difficulty, low efficiency and uncontrollable solution, especially in large-scale data, which is difficult to deal with complex and dynamically changing credit environments.

Method used

Linear relaxation technology and penalty term technology are used to convert the lending risk control model into MCKP form, combined with dominance theory to exclude non-dominance solutions, use optimal slope parameters and greedy algorithm to solve within O(n^2) complexity, and combine integer programming theory and vectorization operations to accelerate data processing to ensure the accuracy and risk tolerance of the solution.

Benefits of technology

Significantly reduce the scale of the problem, improve the solution efficiency and accuracy, and quickly find the optimal solution on super-large-scale MCKP problems, satisfying the controllable risks while improving the efficiency and accuracy of lending risk control.

✦ Generated by Eureka AI based on patent content.
Patent Text Reader

Abstract

The invention discloses a method for solving an MCKP problem and a program product, and the method comprises the steps: carrying out the relaxation processing of a debit and credit risk control model based on a linear relaxation technology and a penalty term technology, and converting the debit and credit risk control model into a form of a multi-choice knapsack problem MCKP; the converted MCKP problem is preprocessed on the basis of the dominance theory, non-dominated solutions are eliminated in the O (n2) complexity, and the optimal slope parameter of the problem is determined on the basis of the non-dominated solutions; on the basis of the split theory, the optimal slope parameter is utilized, the minimum solution space and the maximum solution space are searched in the solution space of the loan combination, and the final solution of the loan combination is solved in the linear complexity of O (n) through a greedy algorithm; calculating a precision control result of a final solution based on a linear relaxation theory and an integer programming theory; vectorization operation is adopted in the whole process to accelerate data processing, it is ensured that each step of operation reaches the theoretically fastest speed, and therefore the efficiency and accuracy of loan risk control processing are improved. The precision is high and controllable, and the precision performance is particularly excellent on the ultra-large scale MCKP problem.
Need to check novelty before this filing date? Find Prior Art