Each function corresponds to an action that you can perform interactively, through the Solver Parameters, Solver Options, and Solver Results dialog boxes of the Solver add-in. .as the function library available within Matlab in combination with the powerful modeling interface and solver kernel included in PK-Sim and MoBi. The following functions can be used to control the Solver add-in from VBA.
If Solver does not appear under Available References, click Browse, and then open Solver.xlam in the \Program Files\Microsoft Office\Office14\Library\SOLVER subfolder. In the Visual Basic Editor, with a module active, click References on the Tools menu, and then select Solver under Available References. In the Add-Ins dialog box, select Solver Add-in, and then click OK.Īfter you have enabled the Solver add-in, Excel will auto-install the Add-in if it is not already installed, and the Solver command will be added to the Analysis group on the Data tab in the ribbon.īefore you can use the Solver VBA functions in the Visual Basic Editor, you must establish a reference to the Solver add-in. In the Manage drop-down box, select Excel Add-ins, and then click Go. The Overflow Blog An algorithm that optimizes for avoiding ennui (Ep. In the Excel Options dialog box, click Add-Ins. Browse other questions tagged excel matlab optimization least-squares minimization or ask your own question. H = sum((q(p, x) - y).Before you can use the Solver VBA functions from VBA, you must enable the Solver add-in in the Excel Options dialog box.Ĭlick the File tab, and then click Options below the Excel tab. Which in this case produces the same result: p_fit_h_nlinfit =ġ2.3018 I have modified the syntax and thanks for your help In the Visual Basic Editor, with a module active, click References on the Tools menu, and then select the Solver. Before you use this function, you must establish a reference to the Solver add-in. Please note that for the default case of function h you can also use nlinfit like so: p_fit_h_nlinfit = nlinfit(x, y, q, p0) Equivalent to clicking Solver in the Data Analysis group and then specifying options in the Solver Parameters dialog box. However, in excel solver the GRG Nonlinear method works very well. x,fval fminsearch(fun,x0) starts at the point x0 and attempts to find a local minimum x of the function described in fun will give you both a vector of your parameters in ‘x’ and the minimised function in ‘fval’. I tried using fminsearch in matlab, and it doesn't converge. It can take multiple input parameters (fzero only takes two it seems). The optimization for you error-function e seems to fail. I want to use regression to fit parameters to a function I define. H = sum((q(p, x) - y).^2) %// default minimizaton function G = sum(sqrt(abs(q(p, x) - y))) %// better take square roots of absolute values
For example, using fminunc because its syntax is a little less cluttered, you could start by defining your cost function in a separate file, named 'NameOfFunction.m'.
%// create the desired error-functions for minimizationĮ = sum((y.^2 - q(p, x)).^2) %// minimization functionį = sum(abs(y - q(p, x))) %// better sum over absolute values You will probably end up finding the solution with fmincon or fminunc in MATLAB. %// create model function q with parameters p(1) = k and p(2) = n In the Solver Parameters dialog box, click.
Select the Show Iteration Results check box to see the values of each trial solution, and then click OK. After you define a problem, in the Solver Parameters dialog box, click Options.
In Excel for Mac 2011: Click the Data tab, under Analysis, click Solver. Also i added the function h, which minimizes the sum of squared errors, which is the default minimization function %// Data In Excel 2016 for Mac: Click Data > Solver. Here comes some code, which techically does, what you want. The option specifies the maximum degree of polynomials for which the solver tries to return explicit solutions. Try to get an explicit solution for such equations by calling the solver with 'MaxDegree'. your minimization "function" g can produce imaginary numbers, as q-y can get negative -> absolute values syms x a eqn x3 + x2 + a 0 solve (eqn, x) ans.your minimization "function" f makes no sense, as positive and negative errors can cancel each other out -> absolute values.your "functions" are not functions, but vectors.