Publications

Papers  in  Refereed  Journals

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11. N. K. Mohanty and R. K. Nayak: A survey on update parameters of nonlinear conjugate gradient methods, Int J. of Mathematical Modelling and Numerical Optimization, Inderscience (2020), ACCEPTED

10. N. K. Mohanty and R. K. Nayak: A new efficient hybrid conjugate gradient method based on LS-DY-HS conjugate gradient parameter, Int J.of Mathematical Modelling and Numerical Optimization, Inderscience (2020), 10.1504/IJMMNO.2020.10031725

9. R. K. Nayak and N. K. Mohanty: Solution of boolean quadratic programming problems by two augmented Lagrangian algorithms based on a continuous relaxation", Journal of Combinatorial Optimization, Springer(2020), Online DOI: 10.1007/s10878-019-00517-8

8. R. K. Nayak and N. K. Mohanty: Improved row-by-row method for binary quadratic optimization problems, Annals of Operations Research, Springer,  DOI doi.org/10.1007/s10479-018-2978-9

7. R. K. Nayak and M. P. Biswal: A low complexity semidefinite relaxation for large-scale MIMO detection, Journal of Combinatorial Optimization, Springer(2018), 35:473–492.

6. Sambit Sekhar Rana, Rupaj Kumar Nayak: A bicriteria mixed-integer programming model for delay management in train timetabling, Journal of Information and Optimization Sciences, Taylor & Francis, 38(2017), 471-479

5. Rupaj Kumar Nayak, Jitamitra Desai: A modified homogeneous potential reduction algorithm for solving the monotone semidefinite linear complementarity problem, Optimization Letters, Springer, 10(2016), 1417-1448

4. Rupaj Kumar Nayak, Harish Kumar Sahoo: A RLT relaxation via Semidefinite cut for the detection of QPSK signaling in MIMO channels, International Journal of Engineering Trends and Technology, 42(8)(2016), 439-446

3. R. K. Nayak, M. P. Biswal, and S. Padhy: An Implementable Predictor-Corrector Method for Solving Semidefinite Programming Problems, Journal of Interdisciplinary Mathematics, Taylor & Francis Vol-13, No.-3,(2014), 223-242.

2. R. K. Nayak, M. P. Biswal, and S. Padhy: An Affine Scaling Method for Solving Network Flow Problems, Journal of Discrete Mathematical Sciences & Cryptography, Taylor & Francis Vol-15, No.-1, (2012), 13-29

1. R. K. Nayak, M. P. Biswal, and S. Padhy: Modification of Karmarkar’s Projective Scaling Algorithm, Applied Mathematics & Computation, Elsevier, 216(2010), 227-235

Papers in International Conference 

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7. Nirmalya Kumar Mohanty and Rupaj Kumar Nayak: A first order method for binary quadratic programs in the combinatorial optimization stream, Anniversary

Conference of the OR society,OR60 Anniversary Conference of the OR society at Lancaster University, Lancaster United Kingdom 2018

6. Nirmalya Kumar Mohanty and Rupaj Kumar Nayak: A new hybrid conjugate gradient parameter and its performance over solving large scale unconstrained problems in machine learning, Research Training Group Algorithmic Optimization (ALOP), University of Trier, Germany 2017

5. Xiaofei Qi, Jitamitra Desai and Rupaj Kumar Nayak: Higher Rank-Order Semidefinite Cutting Planes for Nonconvex QCQPs, INFORMS Annual Meeting, Philadelphia 2015

4. Jitamitra Desai, Xiaofei Qi and Rupaj Kumar Nayak: Minimum Triangle Inequalities and Algorithms for 0-1 QCQPs, INFORMS Annual Meeting, Philadelphia 2015

3. Jitamitra Desai and Rupaj Kumar Nayak: A computational Comparison of SDP-based methods for QCQPs, INFORMS, Minneapolis 2013

2. R. K. Nayak, M. P. Biswal, and S. Padhy: A Semidefinite Relaxation of Portfolio Optimization Problem, International conference of Modeling and Computation, ISI New Delhi, 2008

1. R. K. Nayak, M. P. Biswal, and S. Padhy: Solution of LPP by Karmarkar’s Projective Scaling Method, International Conference & workshop on Operational research as Competitive Edge, ORSI, Calcutta, 2007