LocalEnergyMinimizer

class simtk.openmm.openmm.LocalEnergyMinimizer(*args, **kwargs)

Given a Context, this class searches for a new set of particle positions that represent a local minimum of the potential energy. The search is performed with the L-BFGS algorithm. Distance constraints are enforced during minimization by adding a harmonic restraining force to the potential function. The strength of the restraining force is steadily increased until the minimum energy configuration satisfies all constraints to within the tolerance specified by the Context’s Integrator.

__init__(*args, **kwargs)

Initialize self. See help(type(self)) for accurate signature.

Methods

__init__(*args, **kwargs)	Initialize self.
minimize(context[, tolerance, maxIterations])	minimize(context, tolerance=10) minimize(context)

static minimize(context, tolerance=10, maxIterations=0)

minimize(context, tolerance=10) minimize(context)

Search for a new set of particle positions that represent a local potential energy minimum. On exit, the Context will have been updated with the new positions.

Parameters:

• context (Context) – a Context specifying the System to minimize and the initial particle positions
• tolerance (double) – this specifies how precisely the energy minimum must be located. Minimization will be halted once the root-mean-square value of all force components reaches this tolerance. The default value is 10.
• maxIterations (int) – the maximum number of iterations to perform. If this is 0, minimation is continued until the results converge without regard to how many iterations it takes. The default value is 0.