“ANÁLISIS DE SISTEMAS DE DISTRIBUCIÓN DE GAS POR ... - inicio
“ANÁLISIS DE SISTEMAS DE DISTRIBUCIÓN DE GAS POR ... - inicio “ANÁLISIS DE SISTEMAS DE DISTRIBUCIÓN DE GAS POR ... - inicio
[X,RESNORM,RESIDUAL,EXITFLAG,OUTPUT,LAMBDA]=LSQNONLIN(FUN,X0,...)returns the set of Lagrangian multipliers, LAMBDA, at the solution: LAMBDA.lowerfor LB and LAMBDA.upper for UB.[X,RESNORM,RESIDUAL,EXITFLAG,OUTPUT,LAMBDA,JACOBIAN]=LSQNONLIN(FUN,X0,...) returns the Jacobian of FUN at X.ExamplesFUN can be specified using @:x = lsqnonlin(@myfun,[2 3 4])where MYFUN is a MATLAB function such as:function F = myfun(x)F = sin(x);FUN can also be an anonymous function:x = lsqnonlin(@(x) sin(3*x),[1 4])If FUN is parameterized, you can use anonymous functions to capture the problemdependentparameters. Suppose you want to solve the non-linear least squaresproblem given in the function MYFUN, which is parameterized by its secondargument A. Here MYFUN is an M-file function such asfunction F = myfun(x,a)F = [ 2*x(1) - exp(a*x(1))-x(1) - exp(a*x(2))x(1) - x(2) ];To solve the least squares problem for a specific value of A, first assign the value toA. Then create a one-argument anonymous function that captures that value of Aand calls MYFUN with two arguments. Finally, pass this anonymous function toLSQNONLIN:a = -1; % define parameter firstx = lsqnonlin(@(x) myfun(x,a),[1;1])
FSOLVEsolves systems of nonlinear equations of several variables.FSOLVE attempts to solve equations of the form:F(X)=0where F and X may be vectors or matrices.X=FSOLVE(FUN,X0) starts at the matrix X0 and tries to solve the equations in FUN.FUN accepts input X and returns a vector (matrix) of equation values F evaluated atX.X=FSOLVE(FUN,X0,OPTIONS) minimizes with the default optimization parametersreplaced by values in the structure OPTIONS, an argument created with theOPTIMSET function. See OPTIMSET for details. Used options are Display, TolX,TolFun, DerivativeCheck, Diagnostics, FunValCheck, Jacobian, JacobMult,JacobPattern, LineSearchType, LevenbergMarquardt, MaxFunEvals, MaxIter,DiffMinChange and DiffMaxChange, LargeScale, MaxPCGIter, PrecondBandWidth,TolPCG, TypicalX. Use the Jacobian option to specify that FUN also returns asecond output argument J that is the Jacobian matrix at the point X. If FUN returns avector F of m components when X has length n, then J is an m-by-n matrix whereJ(i,j) is the partial derivative of F(i) with respect to x(j). (Note that the Jacobian J is thetranspose of the gradient of F.)[X,FVAL]=FSOLVE(FUN,X0,...) returns the value of the equations FUN at X.[X,FVAL,EXITFLAG,OUTPUT]=FSOLVE(FUN,X0,...) returns a structure OUTPUTwith the number of iterations taken in OUTPUT.iterations, the number of functionevaluations in OUTPUT.funcCount, the algorithm used in OUTPUT.algorithm, thenumber of CG iterations (if used) in OUTPUT.cgiterations, the first-order optimality (ifused) in OUTPUT.firstorderopt, and the exit message in OUTPUT.message.[X,FVAL,EXITFLAG,OUTPUT,JACOB]=FSOLVE(FUN,X0,...) returns the Jacobian ofFUN at X.
- Page 39 and 40: 2.5. Método de Balances de Presion
- Page 41 and 42: La ecuación modelada para el compr
- Page 43 and 44: S ij = -1 for (pi < pj)El sistema d
- Page 45 and 46: Esto llevó al desarrollo de un esq
- Page 47 and 48: Visual Basic es también un program
- Page 49 and 50: d) CARACTERÍSTICAS.Visual-Basic es
- Page 51 and 52: aprender un lenguaje simple. En est
- Page 53 and 54: Figura 13. Ventana de Archivos .mFi
- Page 55 and 56: CAPITULO IIIMARCO METODOLOGICO1. Di
- Page 57 and 58: Por consiguiente, entre las fuentes
- Page 59 and 60: La ecuación modelada para el compr
- Page 61 and 62: Evaluación del programa y desarrol
- Page 63 and 64: Para iniciar los cálculos, se debe
- Page 65 and 66: 1.1.3. Herramientas Complementarias
- Page 67 and 68: funcCount: 9algorithm: 'trust-regio
- Page 69 and 70: RESIDUAL = 1.0e-007*(-0.2380) y 1.0
- Page 71 and 72: para dar respuestas especificas a c
- Page 73 and 74: problema es necesario estudiar mas
- Page 75 and 76: CONCLUSIONESEl presente trabajo, in
- Page 77 and 78: BIBLIOGRAFÍAMartínez Marcias J. (
- Page 79: ANEXO 1ECUACIONES DE FLUJO
- Page 86 and 87: ANEXO 3MATLAB. FUNCIONES DE APOYOEC
- Page 88 and 89: ANEXO 4MATLAB. FUNCIONES AVANZADAS
- Page 92: ExamplesFUN can be specified using
[X,RESNORM,RESIDUAL,EXITFLAG,OUTPUT,LAMBDA]=LSQNONLIN(FUN,X0,...)returns the set of Lagrangian multipliers, LAMBDA, at the solution: LAMBDA.lowerfor LB and LAMBDA.upper for UB.[X,RESNORM,RESIDUAL,EXITFLAG,OUTPUT,LAMBDA,JACOBIAN]=LSQNONLIN(FUN,X0,...) returns the Jacobian of FUN at X.ExamplesFUN can be specified using @:x = lsqnonlin(@myfun,[2 3 4])where MYFUN is a MATLAB function such as:function F = myfun(x)F = sin(x);FUN can also be an anonymous function:x = lsqnonlin(@(x) sin(3*x),[1 4])If FUN is parameterized, you can use anonymous functions to capture the problemdependentparameters. Suppose you want to solve the non-linear least squaresproblem given in the function MYFUN, which is parameterized by its secondargument A. Here MYFUN is an M-file function such asfunction F = myfun(x,a)F = [ 2*x(1) - exp(a*x(1))-x(1) - exp(a*x(2))x(1) - x(2) ];To solve the least squares problem for a specific value of A, first assign the value toA. Then create a one-argument anonymous function that captures that value of Aand calls MYFUN with two arguments. Finally, pass this anonymous function toLSQNONLIN:a = -1; % define parameter firstx = lsqnonlin(@(x) myfun(x,a),[1;1])
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