Explore MATLAB source code curated for "一阶导数" with clean implementations, documentation, and examples.
The Gaussian function, characterized by its low-pass filtering properties, serves as a wavelet basis function for singularity analysis. Utilizing its first and second derivatives enables precise identification and characterization of abrupt changes in signals, with implementations often involving convolution operations and derivative computations for feature extraction.
The Conjugate Gradient (CG) method serves as an intermediate approach between Steepest Descent and Newton's Method. It leverages only first-order derivative information while overcoming the slow convergence of Steepest Descent and avoiding the computational burden of storing, computing, and inverting the Hessian matrix required by Newton's Method. The CG method is not only one of the most useful techniques for solving large linear systems but also stands as one of the most efficient algorithms for large-scale nonlinear optimization problems. In implementation, CG typically uses iterative updates with conjugate directions computed through recurrence relations rather than matrix operations.
Cubic spline interpolation using the three-moment method for curve fitting, calculating function values at interpolation points, with first and second derivatives