In this paper,we propose a modified two-subgradient extragradient algorithm(MTSEGA)for solving monotone and Lipschitz continuous variational inequalities with the feasible set being a level set of a smooth convex func...In this paper,we propose a modified two-subgradient extragradient algorithm(MTSEGA)for solving monotone and Lipschitz continuous variational inequalities with the feasible set being a level set of a smooth convex function in Hilbert space.The advantage of MTSEGA is that all the projections are computed onto a half-space per iteration.Moreover,MTSEGA only needs one computation of the underlying mapping per iteration.Under the same assumptions with the known algorithm,we show that the sequence generated by this algorithm is weakly convergent to a solution of the concerned problem.展开更多
Using a modified subgradient extragradient algorithm, this paper proposed a novel approach to solving a supply chain network equilibrium model. The method extends the scope of optimisation and improves the accuracy at...Using a modified subgradient extragradient algorithm, this paper proposed a novel approach to solving a supply chain network equilibrium model. The method extends the scope of optimisation and improves the accuracy at each iteration by incorporating adaptive parameter selection and a more general subgradient projection operator. The advantages of the proposed method are highlighted by the proof of strong convergence presented in the paper. Several concrete examples are given to demonstrate the effectiveness of the algorithm, with comparisons illustrating its superior CPU running time compared to alternative techniques. The practical applicability of the algorithm is also demonstrated by applying it to a realistic supply chain network model.展开更多
In this paper, we consider an extragradient thresholding algorithm for finding the sparse solution of mixed complementarity problems (MCPs). We establish a relaxation l1 regularized projection minimization model for t...In this paper, we consider an extragradient thresholding algorithm for finding the sparse solution of mixed complementarity problems (MCPs). We establish a relaxation l1 regularized projection minimization model for the original problem and design an extragradient thresholding algorithm (ETA) to solve the regularized model. Furthermore, we prove that any cluster point of the sequence generated by ETA is a solution of MCP. Finally, numerical experiments show that the ETA algorithm can effectively solve the l1 regularized projection minimization model and obtain the sparse solution of the mixed complementarity problem.展开更多
基金Supported by the National Natural Science Foundation of China(Grant Nos.1187105911801455)+1 种基金Sichuan Science and Technology Program(Grant No.2019YFG0299)General Cultivation Program of China West Normal University(Grant No.20A024)。
文摘In this paper,we propose a modified two-subgradient extragradient algorithm(MTSEGA)for solving monotone and Lipschitz continuous variational inequalities with the feasible set being a level set of a smooth convex function in Hilbert space.The advantage of MTSEGA is that all the projections are computed onto a half-space per iteration.Moreover,MTSEGA only needs one computation of the underlying mapping per iteration.Under the same assumptions with the known algorithm,we show that the sequence generated by this algorithm is weakly convergent to a solution of the concerned problem.
文摘Using a modified subgradient extragradient algorithm, this paper proposed a novel approach to solving a supply chain network equilibrium model. The method extends the scope of optimisation and improves the accuracy at each iteration by incorporating adaptive parameter selection and a more general subgradient projection operator. The advantages of the proposed method are highlighted by the proof of strong convergence presented in the paper. Several concrete examples are given to demonstrate the effectiveness of the algorithm, with comparisons illustrating its superior CPU running time compared to alternative techniques. The practical applicability of the algorithm is also demonstrated by applying it to a realistic supply chain network model.
文摘In this paper, we consider an extragradient thresholding algorithm for finding the sparse solution of mixed complementarity problems (MCPs). We establish a relaxation l1 regularized projection minimization model for the original problem and design an extragradient thresholding algorithm (ETA) to solve the regularized model. Furthermore, we prove that any cluster point of the sequence generated by ETA is a solution of MCP. Finally, numerical experiments show that the ETA algorithm can effectively solve the l1 regularized projection minimization model and obtain the sparse solution of the mixed complementarity problem.
