# 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)

Methods

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

x.__delattr__(‘name’) <==> del x.name

__format__()

default object formatter

__getattribute__

x.__getattribute__(‘name’) <==> x.name

__hash__
__reduce__()

helper for pickle

__reduce_ex__()

helper for pickle

__sizeof__() → int

size of object in memory, in bytes

__str__