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• Operations Research
• Optimization
• Fuzzy Systems
• +4 more

Shu-Cherng Fang is a prominent academic in the fields of Operations Research , Optimization , and Machine Learning . His work spans theoretical advancements and practical applications in Mathematical programming Supply chain network design Fuzzy systems Support vector machines Algorithm development . While specific institutional affiliations and academic rank are not explicitly stated in the provided text, his extensive publication record in high-impact journals indicates a faculty-level role. Research interests include optimization under uncertainty , supply chain logistics , and kernel-free machine learning models . Key trends in recent articles focus on fourth-party logistics (4PL) network design distributionally robust optimization for machine learning mathematical modeling of customer behavior stochastic programming . Co-authors frequently include Min Huang, Zhibin Deng, Jian Luo, and Wenxun Xing, reflecting sustained collaborations. Articles emphasize interdisciplinary approaches combining fuzzy logic , game theory , and computational geometry to solve complex decision-making problems.

• Optimization Theory
• Numerical Optimization
• Newton-Type Methods
• +5 more

Mikhail V. Solodov is a Researcher at the Institute for Pure and Applied Mathematics (IMPA) in Rio de Janeiro, Brazil, where he conducts cutting-edge research in optimization theory and algorithms. His work spans theoretical foundations and practical applications, with significant contributions to Newton-type methods, augmented Lagrangian techniques, and equilibrium problems. Dr. Solodov's educational background includes: Ph.D. in Optimization/Computer Sciences from University of Wisconsin-Madison (1995) M.S. in Computer Sciences from University of Wisconsin-Madison (1992) Diploma (with Honors) in Applied Mathematics from Moscow State University (1991) His research focuses on developing and analyzing optimization algorithms, particularly Newton and Newton-related methods for optimization and variational problems under weakened assumptions, augmented Lagrangian techniques, decomposition methods, and nonsmooth optimization (especially bundle methods). He has made significant contributions to understanding optimization problems with degenerate constraints and has developed relaxed regularity concepts for these challenging problems. Dr. Solodov's recent publications (2022-2025) show a strong trend toward applications in energy markets while maintaining theoretical rigor. His work bridges mathematical theory with practical implementation, demonstrating how optimization theory can solve real-world problems in energy systems and economic equilibrium modeling. The publications reveal increasing focus on equilibrium problems with applications to energy markets, while continuing to advance theoretical understanding of optimization algorithms. His recognition in the field is evident through his editorial appointments: Mathematical Programming, Series A (since 2005) Optimization Methods and Software (since 2002) SIAM Journal on Optimization (2009-2024) Dr. Solodov has also contributed to special journal issues dedicated to prominent figures in optimization, including a special issue on Hierarchical Optimization in Mathematical Programming (2023) and volumes dedicated to the memory of Prof. Naum Shor and Olvi Mangasarian.