Common algorithm assembly in MATLAB

Share the algorithms commonly used in MATLAB language common algorithm assembly (pure). Each algorithm is given in the format of an independent M file. Each function can be called when it is used independently. It is attached with an electronic version of each algorithm and the book. The following is the algorithm directory:

Chapter 1 Interpolation
Function Name	Function
Language	Returns the Laplace interpolation polynomial of a known data point.
Atken	Evaluate the artken interpolation polynomials of known data points
Newton	Finding the mean deviation form of the known data point using the Newton Interpolation Polynomial
Newtonforward	Evaluate the anterior Newton differential interpolation polynomials of known data points
Newtonback	Returns the backward Newton differential interpolation polynomial of a known data point.
Gauss	Evaluate the Gaussian interpolation polynomials of known data points
Hermite	Evaluate the hermit interpolation polynomials of known data points
Subhermite	Evaluate the piecewise cubic hermit interpolation polynomials of known data points and their values at the points of Interpolation
Secsample	Evaluate the quadratic spline interpolation polynomials of known data points and the values at the points of Interpolation
Thrsample1	Evaluate the value of the first class cubic spline interpolation polynomial and its interpolation points of the known data point
Thrsample2	Evaluate the second class of cubic spline interpolation polynomials of known data points and their values at interpolation points
Thrsample3	Evaluate the value of the third class cubic spline interpolation polynomial of the known data point and its interpolation point
Bsample	Evaluate the interpolation of the first class of B-Spline of a known data point
DCS	Use the inverse difference quotient algorithm to calculate the rational fraction of a known data point
Neville	Use the Neville algorithm to calculate the rational fraction of a known data point
Fcz	Use the inverse difference quotient algorithm to calculate the rational fraction of a known data point
DL	Bilinear interpolation for interpolation of known points
DTL	Returns the interpolation of known points by using binary three-point Laplace interpolation.
DH	Returns the zcoordinate of the interpolation point using the triplicate hermit interpolation.
Chapter 1 Function Approximation
Chebyshev	Approximation of known functions using the tangent Polynomial
Legendh	Approximation of known functions using the Leide Polynomial
Pade	Approximation of known functions with rational fraction in the form of padd
LMZ	Use the column Metz algorithm to determine the optimal consistent approximation polynomial of a function
Zjpf	Evaluate the optimum square approximation polynomial of known Functions
Fzz	Approximation of known continuous periodic functions by Fourier Series
DFF	Fourier approximation of discrete period data points
Smartbj	Approximation of known functions using adaptive piecewise linear method
Smartbj	Known functions using adaptive splines (First Class)
Multifit	Polynomial curve fitting of discrete test data points
Lzxec	Linear Least Square Fitting of discrete test data points
Zjzxec	Orthogonal Polynomial Least Square Fitting of discrete test data points
Chapter 2 matrix feature value calculation
Chapoly	Obtain the feature value by finding the root of the matrix feature Polynomial
Pmethod	Power Method for Matrix primary feature values and primary feature vectors
Rpmethod	Primary feature value and primary feature vector of symmetric matrix obtained by using the method accelerated by repex
Spmethod	Evaluate all matrix feature values by using the contraction method
Ipmethod	Evaluate all matrix feature values by using the contraction method
Dimethod	Returns the feature value closest to a constant and its corresponding feature vectors using the displacement inverse power method.
Qrtz	Obtain all the feature values of a matrix using the QR algorithm.
Hessqrtz	Hyenberger qr algorithm for all matrix feature values
Rqrtz	Returns all the feature values of the matrix by using the rpa qr algorithm.
Chapter 2: numerical differentiation
Midpoint	Derivative obtained by the midpoint Formula
Threepoint	Evaluate the derivative of a function by the three-point method
Fivepoint	Evaluate the derivative of a function by the five-point method
Diffbsample	Returns the derivative of a function by cubic spline.
Smartdf	Adaptive Method for Finding the derivative of a function
Cisimpson	Returns the derivative of a function using the Simpson numeric differentiation method.
Richason	Evaluate the derivative of a function by using the Richards Extension Algorithm
Threepoint2	Evaluate the second derivative of a function using the three-point method
Fourpoint2	Evaluate the second derivative of a function by the four-point method
Fivepoint2	Evaluate the second derivative of a function by the five-point method
Diff2bsample	Returns the second derivative of a function by cubic spline.
Chapter 2: Numerical points
Combinetraprl	Compound trapezoid formula for Integral
Intsimpson	Use the Simpson series formula to calculate points
Newtoncotes	Calculate the integral using the Newton-Coz series formula
Intgauss	Calculate the integral using Gaussian Formula
Intgausslada	Calculate the integral using Gaussian channel pulling Formula
Intgausslobato	Calculate the integral using Gaussian-lobaatar Formula
Intsample	Use cubic spline interpolation to calculate points
Intpwc	Using parabolic interpolation for Integral Calculation
Intgausslager	Calculate the integral using Gaussian-lagel Formula
Intgausshermite	Use Gaussian-hermit formula to calculate the integral
Intqbxf1	Calculate the first kind of score
Intqbxf2	Calculate the second kind of score
Dbltraprl	Using the trapezoid formula to calculate the Key Point
Dblsimpson	Calculate the credit using the Simpson formula
Intdbgauss	Using Gaussian formula to calculate multiple credits
Chapter 1 equation Root
Benvlimax	Evaluate the maximum root of a model using the beruli Method
Benvlimin	Evaluate the minimum root of a model by the benuli Method
Halfinterval	Returns a root of the equation using the bipartite method.
HJ	Finding a root of the equation using the golden division method
Stablepoint	Using the fixed point iteration method to find a root of the equation
Atkenstablepoint	Using the Fixed Point Iteration Method accelerated by ekent to find a root of the equation
Steven stablepoint	Using the Fixed Point Iteration Method accelerated by Stephenson to find a root of the equation
Secant	Finding a root of the equation using the string truncation method
Sinlesecant	Finding a root of the equation by means of single-point string Truncation
Dblsecant	Finding a root of the equation by means of double-point string Truncation
Pallsecant	Using the parallel string truncation method to find a root of the equation
Modifsecant	Evaluate a root of the equation using improved string Truncation
Steven secant	Finding a root of the equation using the Stephenson Method
Pyz	Using the split factor method to obtain a quadratic factor of the equation
Parabola	Use the parabolic method to find a root of the equation
Qbs	Evaluate a root of the equation by Chambers
Newtonroot	Finding a root of the equation using the Newton Method
Simplenewton	Using the simplified Newton method to find a root of the equation
Newtondown	Using Newton's downhill method to find a root of the equation
Ysnewton	Calculate all the solid roots of polynomials by the Newton method of successive Compression
Union1	Using Union method 1 to find a root of the equation
Twostep	Using two-step iteration to find a root of the equation
Monte Carlo	Finding a root of the equation using Monte Carlo Method
Multiroot	Find a heavy root for an equation with a heavy Root
Chapter 1 SOLVING NONLINEAR EQUATIONS
Mulstablepoint	Using the fixed point iteration method to find a root of the Nonlinear Equations
Mulnewton	Finding a root of the nonlinear equations by Newton Method
Muldiscnewton	Using the discrete Newton method to find a root of the Nonlinear Equations
Mulmix	A root of the nonlinear equations is obtained by using the Newton-javalone iteration method.
Mulnewtonsor	Using the Newton-sor iteration method to find a root of the Nonlinear Equations
Muldnewton	Using Newton's downhill method to find a root of the Nonlinear Equations
Mulgxf1	The first form of the two-point cut-line method is used to find a root of the nonlinear equations.
Mulgxf2	The second form of the two-point cut-line method is used to find a root of the nonlinear equations.
Mulvnewton	A group of Solutions for Nonlinear Equations Using the quasi-Newton Method
Mulrank1	Using symmetric rank 1 algorithms to find a root of Nonlinear Equations
Muldfp	A group of Solutions for Nonlinear Equations Using D-F-P Algorithms
Mulbfs	Using B-F-S algorithm to find a root of Nonlinear Equations
Mulnumyt	A group of Solutions for Nonlinear Equations Using Numerical Continuation Method
Diffparam1	A group of Solutions for Nonlinear Equations Using Euler's method of Parameter Differentiation
Diffparam2	A group of Solutions for Nonlinear Equations Using the midpoint Integral Method in Parameter Differentiation
Mulfastdown	A group of Solutions for Nonlinear Equations Using the fastest Descent Method
Mulgsnd	A group of solutions for nonlinear equations using Gaussian Newton Method
Mulconj	A group of Solutions for Nonlinear Equations Using the bounded Gradient Method
Muldamp	A group of Solutions for Nonlinear Equations Using the damping least square method
Chapter 2: Direct Method for Solving Linear Equations
Solveuptriangle	The solution of the linear equations Ax = B of the upper triangle Coefficient Matrix
Gaussxqbyorder	Solutions for Linear Equations Ax = B by Gaussian order elimination
Gaussxqlinemain	Gaussian returns the Solution of Linear Equations Ax = B BY COLUMN principal element elimination
Gaussxqallmain	Obtain the solution of the linear equations Ax = B by using the Gaussian PCA Elimination Method
Gaussjordanxq	Gaussian-if the elimination method is used to obtain the Solution of Linear Equations Ax = B
Crout	Solving the linear equations Ax = B using the Kot Decomposition Method
Doolittle	Returns the solution of the linear equations Ax = B by using the dolitler decomposition method.
Sympos1	Solutions for Linear Equations Ax = B Using ll Decomposition Method
Sympos2	The solution of the linear equations Ax = B using the LDL Decomposition Method
Sympos3	The Solution of Linear Equations Ax = B using the improved LDL Decomposition Method
Followup	Query the solutions of linear equations Ax = B
Invaddside	Obtain the Solution of Linear Equations Ax = B by using the inverse method of Edge Addition
Yesf	Returns the Solution of Linear Equations Ax = B using the inverse method obtained by elsov.
Qrxq	Solutions for Linear Equations Ax = B using the QR decomposition method
Chapter 1 Iteration Method for Solving Linear Equations
RS	Finding the Solution of Linear Equations Ax = B using the risamson Iteration Method
CRS	Finding the Solution of Linear Equations Ax = B through the parameter Iteration Method of livensen
GRs	Finding the Solution of Linear Equations Ax = B using the risamson Iteration Method
Kalibana-kibana	Obtain the Solution of Linear Equations Ax = B Through The KNN Iteration Method
Gauseidel	Gaussian-Sader Iteration Method for Solving Linear Equations Ax = B
Sor	Solutions for Linear Equations Ax = B Through superrelaxation Iteration
SSOR	Symmetric successive superrelaxation Iteration Method for Solving Linear Equations Ax = B
JOR	Obtain the solution of the linear equations Ax = B by using the advanced relaxation Iteration Method of the Yahoo!
Twostep	Two-step Iteration Method for Solving Linear Equations Ax = B
Fastdown	Returns the solution of the linear equations Ax = B by the fastest descent method.
Conjgrad	The solution of the linear equations Ax = B using the bounded Gradient Method
Preconjgrad	Evaluate the Solution of Linear Equations Ax = B using the pre-processing conjugate gradient method
BJ	Obtain the Solution of Linear Equations Ax = B by block yake Iteration Method
BGS	Obtain the Solution of Linear Equations Ax = B through the Gaussian-Sader Iteration Method
Bsor	Finding the Solution of Linear Equations Ax = B by block successive superrelaxation Iteration
Chapter 1: Random Number Generation
Pfqz	Use square to obtain a random sequence in France
Mixmod	Generate a random sequence using the mixed coordinality Method
Mulmod1	Generate a random series by using the multiplication and multiplication method 1
Mulmod2	Generate a random series using the multiplication and remainder method 2
Primemod	Generate a random series using the same remainder method of the prime digital model
Powerdist	Generate random series of Exponential Distribution
Laplacedist	Generate a random series of Laplace Distributions
Relaydist	Generate a random series of repex Distributions
Cauthydist	Generate a random sequence of the kernel distribution.
Aeldist	Generate a random series of alilang Distributions
Gaussdist	Generate a random series with a normal distribution
Wbdist	Generate a random series of Western Weber Distributions
Poisondist	Generate a random series of Poisson Distributions
Benulidist	Generate a random series of beruri Distributions
Bgdist	Generate a random series of beruri-Gaussian distributions
Twodist	Generate a random series of binary Distributions
Chapter 2: Special Function compute
Gamafun	Calculate the Gamma function value using the approximation method
Lngama	Use the lanczos algorithm to calculate the natural logarithm of Gamma functions
Beta	Use the Gamma function to calculate the beta function value
Gamap	Use approximation to calculate the value of incomplete Gamma functions
Betap	Use approximation to calculate the value of an incomplete beta function
Bessel	Calculate the Gamma function value using the approximation method
Bessel2	Use the approximation method to calculate the value of the second type of integer-level besell Function
Besselm	Use the approximation method to calculate the value of the first integer-level besell function of the variant.
Besselm2	Use the approximation method to calculate the value of the second type of integer-level besell function of the variant.
Errfunc	Use Gaussian Integral to calculate the error function value
Six	Use Gaussian Integral to calculate the sine integral value
CIX	Calculate cosine integral value using Gaussian Integral
Eix	Use Gaussian points to calculate exponential points
Eix2	Calculate the exponential integral value using the approximation method
Ellipint1	Use Gaussian points to calculate the first class of elliptical points
Ellipint2	Use Gaussian points to calculate the second class of elliptical points
Chapter 2: initial values of Ordinary Differential Equations
Deeuler	Returns the numerical solution of first-order ordinary differential equations using Euler's law.
Deimpeuler	Evaluate the numerical solution of first-order ordinary differential equations using Implicit Euler's Method
Demodifeuler	Use improved Euler's method to obtain the numerical solution of first-order ordinary differential equations
Delgkt2_mid	Use the midpoint method to obtain the numerical solution of first-order ordinary differential equations
Delgkt2_suen	Returns the numerical solution of first-order ordinary differential equations by using the rest method.
Delgkt3_suen	Numerical Solutions of first-order ordinary differential equations using the third-order method of sheun
Delgkt3_kuta	Use the third-order method of Kuta to obtain the numerical solution of first-order ordinary differential equations
Delgkt4_lungkuta	Numerical Solution of first-order ordinary differential equations using the classic longge-Kuta method
Delgkt4_jer	Numerical Solutions of first-order ordinary differential equations using the Kiir Method
Delgkt4_qt	Numerical Solutions of first-order ordinary differential equations using the transformed longge-Kuta method
Delsbrk	Numerical Solutions of first-order ordinary differential equations using the semi-implicit method of rosabunok
DEMs	Use merson's one-step method to obtain the numerical solution of first-order ordinary differential equations
Demiren	Use Milne's method to obtain the numerical solution of first-order ordinary differential equations
Deyds	Numerical Solutions of first-order ordinary differential equations using ADAMS Method
Deycjz_mid	Use the midpoint-trapezoid Prediction Correction Method to obtain the numerical solution of first-order ordinary differential equations
Deycjz_adms	Numerical Solutions of first-order ordinary differential equations using the Adam Prediction Correction Method
Deycjz_adms2	Numerical Solutions of first-order ordinary differential equations using the milun Prediction Correction Method
Deycjz _ YDS	Numerical Solutions of first-order ordinary differential equations using ADAMS Prediction Correction Method
Deycjz _ myds	Using the corrected Adams Prediction Correction Method to obtain the numerical solution of first-order ordinary differential equations
Deycjz_hm	Numerical Solutions of first-order ordinary differential equations using the haming Prediction Correction Method
Dewt	Numerical Solutions of first-order ordinary differential equations by external Method
Dewt_glg	Returns the numerical solution of first-order ordinary differential equations by using the GEG-lag extension method.
Chapter 2: Numerical Solution of Partial Differential Equations
Peellip5	Solution of Laplace equation in five-point difference format
Peellip5m	Work-style difference format solution Laplace Equation
Pehypbyf	Solving Convection Equations in windward format
Pehypblax	Solving the convection equation using the lasx-fredrisieg Formula
Pehypblaxw	Solving the convection equation in the form of drawing-wendelov
Pehypbbw	Solving the convection equation using the beam-wolmingge Formula
Pehypbrich	Solving the convection equation using Richtmyer multi-step format
Pehypbmlw	Solving the convection equation using the lasx-wendelov multi-step format
Pehyplt	Solving the convection equation using MacCormack multi-step format
Pehypb2lf	Solving the initial value problem of two-dimensional convection equations using the Rax-fredrisi Formula
Pehypb2fl	Solving the initial value problem of two-dimensional convection equations using the Rax-fredrisi Formula
Peparabexp	Solving Initial Values of Diffusion Equations in explicit format
Peparabtd	Solving the initial value problem of a diffusion equation in the jump point format
Peparabimp	Solving the initial edge Value Problem of a diffusion equation in implicit format
Peparabkn	Solving the initial edge Value Problem of a diffusion equation in Clarke-Nickerson format
Peparabwegimp	Solving the initial edge value of a diffusion equation in weighted implicit format
Pedkexp	Solving the initial values of the convection diffusion equation in exponential format
Pedksam	Solving the initial value problem of the convection diffusion equation using the samples' kegeform
Chapter 2: Data Statistics and Analysis
Multilinereg	Linear regression is used to estimate the linear relationship between a dependent variable and multiple independent variables.
Polyreg	Use polynomial regression to estimate the polynomial relationship between a dependent variable and an independent variable
Comppoly2reg	Estimating the relationship between a dependent variable and two independent variables using quadratic full regression
Collectanaly	Use the shortest distance algorithm system clustering to cluster Samples
Distgshanalysis	Use two types of Fisher Discriminant to classify Samples
Mainanalysis	Principal component analysis of samples

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