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Finds the values for the x arguments, specified as a list, that maximize the expr expression with optional linear constraints. You must specify lower and upper bounds in parentheses for each argument.
In the following messages, A is a matrix of coefficients. x = [x1, x2, ...] is the vector of arguments. b is a vector that forms the right side of the expression.
p sets the tolerance for the convergence criterion. The default tolerance is 10^5.
Finds the values for the x arguments, specified as a list, that minimize the expr expression with optional linear constraints. You must specify lower and upper bounds in parentheses for each argument.
In the following messages, A is a matrix of coefficients. x = [x1, x2, ...] is the vector of arguments. b is a vector that forms the right side of the expression.
p sets the tolerance for the convergence criterion. The default tolerance is 10^5.
Fits a function to go through the three points, suitable for defining the desirability of a set of response variables (y’s). yVector and desireVector are matrices with three values, corresponding to the three points defining the desirability function. The actual function depends on whether the desire values are in the shape of a larger-is-better, smaller-is-better, target, or antitarget.
Finds the values for the x arguments, specified as a list, that maximize the expression expr. You can specify lower and upper bounds in parentheses for each argument. Additional arguments for the function enable you to set the maximum number of iterations, tolerance for convergence, and view more details about the optimization. The Newton-Raphson method is used when an analytical derivative is found for the Hessian. Otherwise, the Symmetric-Rank One method (SR1), a quasi-Newton method, is used.
p sets the tolerance for the convergence criterion. The default tolerance is 10^-8.
Finds the values for the x arguments, specified as a list, that minimize the expression expr. You can specify lower and upper bounds in parentheses for each argument. Additional arguments for the function enable you to set the maximum number of iterations, tolerance for convergence, and view more details about the optimization. The Newton-Raphson method is used when an analytical derivative is found for the Hessian. Otherwise, the Symmetric-Rank One method (SR1), a quasi-Newton method, is used.
p sets the tolerance for the convergence criterion. The default tolerance is 10^-8.

Help created on 3/19/2020