Conjugate Gradient Method And Its Extensions
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In this project we investigate conjugate gradient method and its extension to solve unconstrainedrnminimization problems. There are two important methods for solving linear equationsrnand nonlinear optimization problems. The performance of the linear conjugate gradientrnmethod is tied to the distribution of the eigenvalues of the coe cient matrix. Nonlinear conjugaterngradient method is used for solving large-scale nonlinear optimization problems andrnhas wide applications in many elds. It is also discussed how to use the result to obtain thernconvergence of the famous Fletcher-Reeves, and Polak-Ribiere conjugate gradient methods.rnAnd comparisons are made among the algorithms of the steepest descent method, Newton'srnmethod and conjugate gradient method for quadratic and nonquadratic problems.
Subsurface Intelligence & Critical Mineral Exploration
Modern Geology projects now focus on Machine Learning in Mineral Targeting, Carbon Capture & Storage (CCS) Geologic Modeling, and Critical Mineral Systems (Lithium, REEs). If your research involves Hydrogeological Connectivity, Seismic Inversion, or Geotechnical Site Characterization, ensure your analysis follows the JORC or NI 43-101 reporting standards and utilizes robust 3D Subsurface Visualization and Geochemical Fingerprinting frameworks.
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