Multiobjective Markov chains optimization problem with strong Pareto frontier: Principles of decision making
作者：
Highlights：
• We formulate a regularized penalty function solution for the multi-objective constrained problem.
• We prove that exists a solution of the original problem with minimal weighted norm which is unique.
• We suggest a projection-gradient algorithm for computing the penalty function.
• We prove the convergence of the gradient method and the rate of convergence.
• We present a method that make the Pareto frontier more useful as decision support system.
摘要
•We formulate a regularized penalty function solution for the multi-objective constrained problem.•We prove that exists a solution of the original problem with minimal weighted norm which is unique.•We suggest a projection-gradient algorithm for computing the penalty function.•We prove the convergence of the gradient method and the rate of convergence.•We present a method that make the Pareto frontier more useful as decision support system.
论文关键词：Multi-objective,Decision making,Pareto,Tikhonov’s regularization,Markov chains
论文评审过程：Received 16 February 2016, Revised 12 October 2016, Accepted 13 October 2016, Available online 13 October 2016, Version of Record 20 October 2016.
论文官网地址：https://doi.org/10.1016/j.eswa.2016.10.027
