pathsim.optim.newton module

class pathsim.optim.newton.NewtonRaphsonAD

Bases: object

This class implements the newton raphson method using pathsims automatic differentiation framework to compute anlytical jacobians.

solve(func, x0, iterations_max=100, tolerance=1e-06)

Solve the function ‘func’ with initial value ‘x0’ up to a certain tolerance.

Parameters:

• func (callable) – function to solve

• x0 (numeric) – starting value for solution

• iterations_max (int) – maximum number of solver iterations

• tolerance (float) – convergence condition

Returns:

• x (numeric) – solution

• res (float) – residual

• i (int) – iteration count

reset()

step(x, g, *args, **kwargs)

Perform one newton-raphson step

Parameters:

• x (float, array) – current solution

• g (float, array) – current evaluation of g(x)

Returns:

• x (float, array) – new solution

• res (float) – residual norm, fixed point error

class pathsim.optim.newton.GaussNewtonAD

Bases: NewtonRaphsonAD

This class implements the gauss newton method using pathsims automatic differentiation framework to compute anlytical jacobians.

step(x, g, *args, **kwargs)

Perform one gauss-newton step

Parameters:

• x (float, array) – current solution

• g (float, array) – current evaluation of g(x)

Returns:

• x (float, array) – new solution

• res (float) – residual norm, fixed point error

class pathsim.optim.newton.LevenbergMarquardtAD

Bases: NewtonRaphsonAD

This class implements the levenberg marquardt algorithm using pathsims automatic differentiation framework to compute anlytical jacobians.

reset()

step(x, g, *args, **kwargs)

Perform one LM step

Parameters:

• x (float, array) – current solution

• g (float, array) – current evaluation of g(x)

Returns:

• x (float, array) – new solution

• res (float) – residual norm, fixed point error