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  2. Prediction by partial matching - Wikipedia

    en.wikipedia.org/wiki/Prediction_by_partial_matching

    Prediction by partial matching (PPM) is an adaptive statistical data compression technique based on context modeling and prediction. PPM models use a set of previous symbols in the uncompressed symbol stream to predict the next symbol in the stream. PPM algorithms can also be used to cluster data into predicted groupings in cluster analysis.

  3. Parks–McClellan filter design algorithm - Wikipedia

    en.wikipedia.org/wiki/Parks–McClellan_filter...

    Rather, the initial frequency set {ω i} and the desired function D(ω i) defined the pass and stop band implicitly. Unlike previous attempts to design an optimal filter, the Maximal Ripple algorithm used an exchange method that tried to find the frequency set { ω i } where the best filter had its ripples. [ 1 ]

  4. Helmert transformation - Wikipedia

    en.wikipedia.org/wiki/Helmert_transformation

    μ – scale factor, which is unitless; if it is given in ppm, it must be divided by 1,000,000 and added to 1. R – rotation matrix. Consists of three axes (small [clarification needed] rotations around each of the three coordinate axes) r x, r y, r z. The rotation matrix is an orthogonal matrix. The angles are given in either degrees or radians.

  5. Quadratically constrained quadratic program - Wikipedia

    en.wikipedia.org/wiki/Quadratically_constrained...

    There are two main relaxations of QCQP: using semidefinite programming (SDP), and using the reformulation-linearization technique (RLT). For some classes of QCQP problems (precisely, QCQPs with zero diagonal elements in the data matrices), second-order cone programming (SOCP) and linear programming (LP) relaxations providing the same objective value as the SDP relaxation are available.

  6. Split-step method - Wikipedia

    en.wikipedia.org/wiki/Split-step_method

    First, the method relies on computing the solution in small steps, and treating the linear and the nonlinear steps separately (see below). Second, it is necessary to Fourier transform back and forth because the linear step is made in the frequency domain while the nonlinear step is made in the time domain .

  7. Nelder–Mead method - Wikipedia

    en.wikipedia.org/wiki/Nelder–Mead_method

    Nelder–Mead (Downhill Simplex) explanation and visualization with the Rosenbrock banana function; John Burkardt: Nelder–Mead code in Matlab - note that a variation of the Nelder–Mead method is also implemented by the Matlab function fminsearch. Nelder-Mead optimization in Python in the SciPy library.

  8. This Is Our Most Saved Casserole Of 2024

    www.aol.com/most-saved-casserole-2024-142054847.html

    If there is a dish that defines Southern dining, it is the beloved casserole.From weeknight dinners to family potlucks to holiday celebrations, casseroles are a feel-good meal that brings people ...

  9. Euler–Maruyama method - Wikipedia

    en.wikipedia.org/wiki/Euler–Maruyama_method

    In Itô calculus, the Euler–Maruyama method (also simply called the Euler method) is a method for the approximate numerical solution of a stochastic differential equation (SDE). It is an extension of the Euler method for ordinary differential equations to stochastic differential equations named after Leonhard Euler and Gisiro Maruyama. The ...