(Note that the Jacobian J is the transpose of the gradient of F.) If fun returns a vector (matrix) of m components and x has length n, where n is the length of x0, then the Jacobian J is an m-by-n matrix where J(i,j) is the partial derivative of F(i) with respect to x(j). % Jacobian of the function evaluated at x Note that by checking the value of nargout the function can avoid computing J when fun is called with only one output argument (in the case where the optimization algorithm only needs the value of F but not J). Then the function fun must return, in a second output argument, the Jacobian value J, a matrix, at x. If the Jacobian can also be computed and the Jacobian parameter is 'on', set by x = myfun is a MATLAB function such as.The function fun can be specified as a function handle.
fun is a function that accepts a vector x and returns a vector F, the nonlinear equations evaluated at x. The nonlinear system of equations to solve. This section provides function-specific details for fun and options: Returns the Jacobian of fun at the solution x.įunction Arguments contains general descriptions of arguments passed in to fsolve. Returns a structure output that contains information about the optimization. Returns a value exitflag that describes the exit condition.
Returns the value of the objective function fun at the solution x. Pass an empty matrix for options to use the default values for options. Passes the problem-dependent parameters P1, P2, etc., directly to the function fun. Minimizes with the optimization parameters specified in the structure options. Starts at x0 and tries to solve the equations described in fun. = fsolve(.)įsolve finds a root (zero) of a system of nonlinear equations. Fsolve (Optimization Toolbox) Optimization Toolboxįor x, where x is a vector and F(x) is a function that returns a vector value.
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