# LocalEnergyMinimizer Class Reference

Given a Context, this class searches for a new set of particle positions that represent a local minimum of the potential energy. More...

Inherits simtk::openmm::openmm::_object.

List of all members.

## Public Member Functions

def | __init__
def | minimize
| minimize(Context context, double tolerance = 1, int maxIterations = 0) minimize(Context context, double tolerance = 1) minimize(Context context)
def | __del__
| __del__(self)

## Detailed Description

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.

## Member Function Documentation

def __del__ | ( |  | self | ) |

__del__(self)

def __init__ | ( |  | self,
|  |  | args,
|  |  | kwargs |
| ) |  |  |

def minimize | ( |  | args | ) |

minimize(Context context, double tolerance = 1, int maxIterations = 0) minimize(Context context, double tolerance = 1) minimize(Context 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 | a Context specifying the System to minimize and the initial particle positions
  | tolerance | 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 1.
  | maxIterations | 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.

The documentation for this class was generated from the following file:

- openmm.py