Suraj Salihu
Papers
1
Total Citations
17
H-Index
1
About
Suraj Salihu is a rising figure in computational optimization, whose work centers on developing efficient numerical algorithms for unconstrained optimization problems. His primary research focus lies in the design and analysis of hybrid conjugate gradient methods, which are critical for solving large-scale optimization tasks in fields ranging from engineering to machine learning. Salihu’s most cited paper, “A Hybrid Conjugate Gradient Method for Unconstrained Optimization with Application” (2023, 17 citations), introduces a novel hybrid algorithm that optimally selects a modulating non-negative parameter from the Dai-Liao conjugacy condition. By constructing a convex combination of the Hestenes-Stiefel and Dai-Yuan conjugate gradient methods, his approach achieves enhanced convergence and robustness, offering a practical tool for real-world applications. This contribution addresses a longstanding challenge in optimization: balancing theoretical performance with computational efficiency. Though early in his career, Salihu’s work has already garnered attention for its elegant synthesis of established techniques, and his paper serves as a valuable resource for researchers seeking improved solvers. His achievements mark him as a promising contributor to the field, with potential for significant impact in both theoretical and applied optimization.
Research Focus
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Top Papers
- 1