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Updated: May 1, 2026

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
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A simple SQP algorithm for constrained finite minimax problems.

Lirong Wang1, Zhijun Luo2

  • 1The Department of Information Science and Engineering, Hunan University of Humanities, Science and Technology, Loudi 417000, China.

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|April 1, 2014
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Summary
This summary is machine-generated.

A new sequential quadratic programming method efficiently solves constrained minimax problems. This algorithm avoids common issues and achieves reliable convergence, demonstrating successful performance in numerical tests.

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Area of Science:

  • Optimization
  • Numerical Analysis
  • Mathematical Programming

Background:

  • Constrained minimax problems present significant challenges in optimization.
  • Existing methods may suffer from convergence issues or the Maratos effect.

Purpose of the Study:

  • To propose a novel and simple sequential quadratic programming (SQP) method.
  • To address the complexities of solving constrained minimax problems effectively.

Main Methods:

  • Introducing an auxiliary variable at each iteration.
  • Solving a single quadratic programming (QP) subproblem for the descent direction.
  • Utilizing a corresponding QP to obtain a high-order revised direction, mitigating the Maratos effect.

Main Results:

  • The proposed algorithm demonstrates global and superlinear convergence under mild conditions.
  • Numerical results confirm the successful application and effectiveness of the algorithm.

Conclusions:

  • The developed SQP method offers an efficient approach to constrained minimax problems.
  • The algorithm successfully overcomes limitations of previous methods and achieves robust convergence.