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newton method
- function p=newton(f_str,df_str,p0) tol=0.00001; f = inline(f_str); df= inline(df_str); while 1 p=p0-f(p0)/df(p0); if abs(p-p0)<tol, break; end p0=p; disp(p0); end disp(p)
TVAL3
- %TVDENOISE Total variation grayscale and color image denoising % u = TVDENOISE(f,lambda) denoises the input image f. The smaller % the parameter lambda, the stronger the denoising. % % The output u approximately minimizes the Rudin-Osher-Fatemi (ROF)
TVL1_HCS_v1
- % May 2010 % This matlab code implements TVL1 based Hybrid Compressive Sensing using LSQR. % Only suitable the small scale data due to the costly storage and computation. % % A - M x N measurement matrix: random sampling alone or hybrid sampling (ran
ALG021
- 解方程，具体见英文描述，给一个连续方程，解出f(x) = 0 在一定范围的解- To find a solution to f(x) = 0 given the continuous function f on the interval [a,b], where f(a) and f(b) have opposite signs: INPUT: endpoints a,b tolerance TOL maximum
ALG022
- 解方程，给出一个连续函数接f(x)=0的值，前提是给一个附近的值- FIXED-POINT ALGORITHM 2.2 To find a solution to p = g(p) given an initial approximation p0 INPUT: initial approximation p0 tolerance TOL maximum number of iterations NO.
ALG023
- NEWTON-RAPHSON求解一个连续方程，f（X）=0，前提给一个初始值- NEWTON-RAPHSON ALGORITHM 2.3 To find a solution to f(x) = 0 given an initial approximation p0: INPUT: initial approximation p0 tolerance TOL maximum n
ALG024
- SECANT法求解一个连续方程，f(x) = 0，给两个初始值- SECANT ALGORITHM 2.4 To find a solution to the equation f(x) = 0 given initial approximations p0 and p1: INPUT: initial approximation p0, p1 tolerance TOL maximum number o
ALG025
- 解方程，给出一个连续函数接f(x)=0的值，前提是给两个附近的值，且符号相反 - METHOD OF FALSE POSITION ALGORITHM 2.5 To find a solution to f(x) = 0 given the continuous function f on the interval [p0,p1], where f(p0) and f(p1) have opposite signs: INPUT
fixed_point_systems
- Fixed-point for functions of several variables -Function fixed_point_systems(x0, N) approximates the solution of a system of nonlinear equations F(x) = (f1(x), f2(x), ..., fn(x)) = 0 rewritten in the fixed-point form x = G(x) = (g1(x), g2(x), ..., gn
fixed_point
- Fixed-Point iteration-Function fixed_point(p0, N) approximates the solution of an equation f(x) = 0, rewritten in the form x = g(x), which is a sub-function the user has to enter. the call to the function fixed_point(p0, N) returns the root of the eq
RBFNN
- Three function for RBF neural network, using OLS,Rand and SGA function [newcenter,sigma,W,yh,rmse]=rbfols(p,t,tol) p 為輸入資料點，N×K矩陣，N是輸入資料維度，K是資料點數 t 為目標輸出值，1×K矩陣 tol 為指定容忍度或正確率 centers選定中心點，N×nc矩陣 sigma為 ? 值 W為輸出層權重，nc×1矩陣 y
Rombegrg
- 录入程序代码，并对该实验代码进行调试，检查程序代码中的语法和语义错误。 编写函数f（x）的代码如下： Romberg算法¨ function z=f(x) if (x~=0) z=sin(x)/x else z=1 end 备注：在实验代码中，首先输入必要的变量的值如下： a=0 b=1 tol=1e-8 待查询检查通过，开始输入执行代码 设置格式format short g查看u的值和sin（u）的值： -Inp
newton
- 非线性方程为xe(x)括号的x为e的指数-1=0. 要求输入初值x0.和精度tol及最大循环次数N. 输出利用newton迭代法解出的近似根-Nonlinear equations for the xe (x) x as e brackets index-1 = 0. Asked to enter the initial value x0. And accuracy and the maximum number of cycles N. tol output using newton i
asymppdc
- 这是第一版的AsymPDC工具包。用来处理PDC，gPDC和iPDC有关内容。运行环境为Matlab,并且要求至少Matlab中预装了3个工具箱：控制系统，信号处理和统计工具箱。-This is the first public release of AsympPDC package. It deals with the asymptotic statistics for PDC, gPDC and iPDC. AsympPDC runs under Matlab and is a pra
bisection
- Step 1: Set i=1 FA=f(a) Step 2: while i≤ No do step 3-6. Step 3 set p=(a+b)/2 FP=f(p) Step 4 if FP<TOL or (b-a)/2<TOL then OUTPUT(p) STOP. Step 5 set i=i+1 Step 6 if FA.FP > 0 then set a=p FA=FP else set b=p. Step 7 OUTPUT (‘
bisection_new
- Step 1: Set i=1 FA=f(a) Step 2: while i≤ No do step 3-6. Step 3 set p=(a+b)/2 FP=f(p) Step 4 if FP<TOL or (b-a)/2<TOL then OUTPUT(p) STOP. prepared by Razana Alwee 24 Algorithm Step 5 set i=i+1 Step 6 if FA.FP > 0
Steepest-descent-method
- 最优化算法，最速下降法,matlab函数实现 function [istatus,xm,ym,lamda]=quickestdown(y,x0,lamda0, tol, maxIter) 最速下降法 输入：fun--目标函数 x--变量 x0---初始位置 lamda0--初始步长 tol---精度 maxIter--最大迭代次数 -Steepest descent method
GM_EM
- 不错的GM_EM代码。用于聚类分析等方面。- GM_EM- fit a Gaussian mixture model to N points located in n-dimensional space. Note: This function requires the Statistical Toolbox and, if you wish to plot (for k = 2), the function error_ellipse Elem
InteriorPoint
- Solve a problem of Linear Program by the Interior Points methods. Function [x, Al, Z] = InteriorPoint(c0, x0, A0, b0, alpha, tol) Inputs - c0 -> initial coef. of O.F. x0 -> initial solution A0 -> initial con
TOLBOL
- 利用tol和bol进行多基地的定位，最后给出GDOP图(TOL and BOL are used to locate for multi base, and finally the GDOP diagram is given)