Rupaj Kumar Nayak
International Institute of Information Technology Bhubaneswar, Nanyang Technological University, Utkal University
About
Dr. Rupaj Kumar Nayak is a retired Associate Professor of Mathematics from the School of Basic Sciences, IIIT Bhubaneswar. His areas of expertise include Optimization Theory, Operations Research, and Mathematical Programming. His research and teaching have focused on optimization methods, particularly nonconvex optimization and optimization techniques applied to data analysis.
His research addresses a broad range of optimization problems involving continuous and discrete decision variables. These problems may involve a single decision-maker, with constraints that are deterministic or stochastic in nature. He has developed and applied advanced optimization methodologies to diverse areas, including network flow optimization, hub location management and other real-world decision-making problems.
Dr. Nayak received his Ph.D. in Mathematics from Utkal University in 2008 and subsequently completed postdoctoral research at Nanyang Technological University (NTU), Singapore. He has published extensively in the field of optimization and has served as a reviewer for several scholarly journals, including the Journal of the Orissa Mathematical Society. His research contributions span theoretical developments in optimization as well as applications to data analysis and interdisciplinary problems.
Employment
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Nanyang Technological University Research Fellow2012 - 2013
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International Institute of Information Technology Bhubaneswar Teaching & Research2009 - Present
Education
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Utkal University Ph.D2002 - 2008
Projects & Funding
Projects & funding information is unavailable.
Publications (12)
- THE TIGHTNESS OF STRENGTHENED SEMIDEFINITE RELAXATIONS FOR THE BOOLEAN QUADRATIC PROGRAMMING PROBLEM WITH GENERALIZED UPPER BOUND CONSTRAINTS Save
- A COMPUTATIONAL STUDY ON THE GLOBAL SOLUTION OF NONCONVEX QUADRATIC CONSTRAINTS AND QUADRATIC PROGRAMMING PROBLEMS Save
- A survey on update parameters of nonlinear conjugate gradient methods Save
- Solution of boolean quadratic programming problems by two augmented Lagrangian algorithms based on a continuous relaxation Save
- A new efficient hybrid conjugate gradient method based on LS-DY-HS conjugate gradient parameter Save
- A low complexity semidefinite relaxation for large-scale MIMO detection Save
- Improved row-by-row method for binary quadratic optimization problems Save
- A bicriteria mixed-integer programming model for delay management in train timetabling Save
- A modified homogeneous potential reduction algorithm for solving the monotone semidefinite linear complementarity problem Save
- An implementable predictor-corrector method for solving semidefinite programming problems Save