sellar_state_MDF_optimize.py

Optimize the Sellar problem using SLSQP. This version elminates the cycle and replaces it with an implicit component.

class openmdao.examples.sellar_state_MDF_optimize.SellarDis1[source]

Bases: openmdao.core.component.Component

Component containing Discipline 1.

linearize(params, unknowns, resids)[source]

Jacobian for Sellar discipline 1.

solve_nonlinear(params, unknowns, resids)[source]

Evaluates the equation y1 = z1**2 + z2 + x1 - 0.2*y2

class openmdao.examples.sellar_state_MDF_optimize.SellarDis2[source]

Bases: openmdao.core.component.Component

Component containing Discipline 2.

linearize(params, unknowns, resids)[source]

Jacobian for Sellar discipline 2.

solve_nonlinear(params, unknowns, resids)[source]

Evaluates the equation y2 = y1**(.5) + z1 + z2

class openmdao.examples.sellar_state_MDF_optimize.SellarStateConnection[source]

Bases: openmdao.core.group.Group

Group containing the Sellar MDA. This version uses the disciplines with derivatives.

class openmdao.examples.sellar_state_MDF_optimize.StateConnection[source]

Bases: openmdao.core.component.Component

Define connection with an explicit equation

apply_nonlinear(params, unknowns, resids)[source]

Don’t solve; just calculate the residual.

linearize(params, unknowns, resids)[source]

Analytical derivatives.

solve_nonlinear(params, unknowns, resids)[source]

This is a dummy comp that doesn’t modify its state.