General Optimizer

The General Optimizer is used to find the minimum, maximum, or root of the model. This can be used to find the optimal solution (sample) of the quantity of interest.

Warning

Using a black-box approach to optimization does not guarantee that the global minimum, maximum, or root has been found. To overcome this, see Attempts

Minimize and Maximize

To find the maximum or minimum of the model, select the operation from the Operation combo-box. Maximization is treated exactly like minimization except the model is multiplied by a -1.

There are two global methods and 8 local methods implemented in scipy.optimize that can be used. Depending on the method selected, other options maybe be exposed.

Global methods:

• Basin Hopping

• Differential Evolution

• Simplicial homology global optimization (SHGO)

• Dual annealing

Local methods (select local in the Method combo-box):

• Nelder-Mean

• Powell

• Conjugate Gradient (CG)

• quasi-Newton method of Broyden, Fletcher, Goldfarb, and Shanno (BFGS)

• L-BFGS-B

• truncated Newton algorithm (TNC)

• Constrained Optimization BY Linear Approximation (COBYLA)

• Sequential Least SQuares Programming (SLSQP)

Root

To find the root of the model, select the find value (root) from the Operation combo-box. The method can be selected using the Method combo-box. The available root finding methods are:

• Powell hybrid method (hybr)

• Levenberg-Marquardt (lm)

• Broyden’s first Jacobian approximation (broyden1)

• Broyden’s second Jacobian approximation (broyden2)

• Anderson mixing (anderson)

• Scalar Jacobian approximation (linearmixing)

• Diagonal Broyden Jacobian approximation (diagbroyden)

• Tuned diagonal Jacobian approximation (exitingmixing)

• Krylov approximation for inverse Jacobian (krylov)

• Derivative-free spectral method (df-sane)

Enter the value to find in the Value line-edit.

Attempts

As a tool to overcome the issue of the black-box approach to optimization not guaranteeing that the global minimum, maximum, or root has been found, the operation can be run multiple times with a random initial condition. The number of attempts can be entered in the Attempts spin-box. numpy.random.random is used to pick the random initial condition within the bounds of the fit model. Each random initial condition is then used in the operation, with the solutions being populated in the table. If the independently run solutions converge, then the confidence that the converged solution is correct is increased.

Optimize

Once the desired options have been selected, press the Optimize button. This could take a significant amount of time to complete, especially if many attempts are being performed. When finished, the sample and value for each attempt will be displayed in the table. This table can be sorted by pressing the column headers.

Plot

The plot tab will show the sampled model and all the optimization attempts in either 3D or 2D. The plot type can be changed by selecting the plot from the Plot combo-box. If there are more than two variables, the currently x and y axis can be changed by selecting the variables from X Axis and Y axis combo-boxes.

Parallel Plot

Sometimes the optimization routine will find different optimums. To help visualize these optimums and look for groupings of values, the parallel plot plots each optimization attempt. Options across the top provide tools to help visualize the attempts.