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NEURAL NETWORKS
PRINCIPAL COMPONENT ANALYSIS OPTIMIZATION (NONLINEAR AND QUADRATIC) Optimization (nonlinear and quadratic) L-BFGS and CG. Unconstrained optimization. Optional numerical differentiation. Levenberg-Marquardt algorithm. Unconstrained or box/linearly constrained optimization. Optional numerical differentiation. Box and linearly constrained optimization. Linearly equality/inequality (and box) constrainedoptimization.
DENSE SOLVERS FOR LINEAR SYSTEMS C++ version is the most efficient, pure NET version and versions in other languages have significantly lower performance. Number of right-hand parts. ALGLIB solver is optimized for systems with one right-hand part or for systems with low number of right-hand parts. If right-hand part is too large then you'll experience a performance drop LEAST SQUARES FITTING (LINEAR/NONLINEAR) POLYNOMIAL INTERPOLATION SINGULAR SPECTRUM ANALYSIS (SSA) SINGULAR VALUE DECOMPOSITION ALGLIB - C++/C# NUMERICAL ANALYSIS LIBRARYFREE EDITIONCOMMERCIAL EDITIONDOCSFAQFORUMABOUT US About ALGLIB. ALGLIB is a cross-platform numerical analysis and data processing library. It supports several programming languages (C++, C#, Delphi) and several operating systems (Windows and POSIX, including Linux).ALGLIB features include: NONLINEAR AND POLYNOMIAL EQUATIONS ALGLIB® - numerical analysis library, 1999-2021. ALGLIB is a registered trademark of the ALGLIB Project. Policies for this site: privacy policy, trademark policy.NEURAL NETWORKS
PRINCIPAL COMPONENT ANALYSIS OPTIMIZATION (NONLINEAR AND QUADRATIC) Optimization (nonlinear and quadratic) L-BFGS and CG. Unconstrained optimization. Optional numerical differentiation. Levenberg-Marquardt algorithm. Unconstrained or box/linearly constrained optimization. Optional numerical differentiation. Box and linearly constrained optimization. Linearly equality/inequality (and box) constrainedoptimization.
DENSE SOLVERS FOR LINEAR SYSTEMS C++ version is the most efficient, pure NET version and versions in other languages have significantly lower performance. Number of right-hand parts. ALGLIB solver is optimized for systems with one right-hand part or for systems with low number of right-hand parts. If right-hand part is too large then you'll experience a performance drop LEAST SQUARES FITTING (LINEAR/NONLINEAR) POLYNOMIAL INTERPOLATION SINGULAR SPECTRUM ANALYSIS (SSA) SINGULAR VALUE DECOMPOSITION NONLINEAR AND POLYNOMIAL EQUATIONS ALGLIB® - numerical analysis library, 1999-2021. ALGLIB is a registered trademark of the ALGLIB Project. Policies for this site: privacy policy, trademark policy. TIME SERIES ANALYSIS Time series analysis. Moving average filters (SMA, EMA, LRMA) Different kinds of moving average filters. Singular spectrum analysis (SSA) Filtering and prediction with SSA. OPTIMIZATION (NONLINEAR AND QUADRATIC) Optimization (nonlinear and quadratic) L-BFGS and CG. Unconstrained optimization. Optional numerical differentiation. Levenberg-Marquardt algorithm. Unconstrained or box/linearly constrained optimization. Optional numerical differentiation. Box and linearly constrained optimization. Linearly equality/inequality (and box) constrainedoptimization.
NEURAL NETWORK ENSEMBLES Neural network ensembles. This page contains description ensembles of neural networks and their implementation in ALGLIB. Prior to reading this page, it is necessary that you look through the paper on the general principles of data analysis methods.It contains important information which, to avoid duplication (as it is of great significance for each algorithm in this section), is moved to a DENSE SOLVERS FOR LINEAR SYSTEMS Dense solvers for linear systems. Systems of linear equations Ax=b may be divided into two classes: those with square non-degenerate A and those with rectangular possibly rank deficient A.ALGLIB package have subroutines for both types of problems. SINGULAR SPECTRUM ANALYSIS (SSA) Singular spectrum analysis (SSA) Singular spectrum analysis (SSA, also known as Caterpillar-SSA) is a non-parametric time series analysis method. It can be used to filter out noise components or to predict future values. ALGLIB package includes highly optimized SSA implementation available in several programming languages, including:DECISION FOREST
Decision forest. This page contains a brief description of the RDF classification and regression algorithm. Prior to reading this page, it is necessary that you look through the paper on the general principles of data analysis methods.It contains important information which, to avoid duplication (as it is of great significance for each algorithm in this section), is removed to a separate page. UNCONSTRAINED OPTIMIZATION: L-BFGS AND CG About algorithms. ALGLIB package contains three algorithms for unconstrained optimization: L-BFGS, CG and Levenberg-Marquardt algorithm . This article considers first two algorithms, which share common traits: they solve general form optimization problem (target function has no special structure) MOVING AVERAGE FILTERS (SMA, EMA, LRMA) Moving average filters SMA (simple moving average) Simple moving average filter, denoted as SMA(k), is a finite impulse response filter.For any moment t it returns average of previous k values (or tvalues, for t
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