qiskit.aqua.algorithms.ClassicalCPLEX¶

class ClassicalCPLEX(operator, timelimit=600, thread=1, display=2)[source]¶

The Classical CPLEX algorithm (classical).

This algorithm uses the IBM ILOG CPLEX Optimization Studio along with its separately installed Python API to solve optimization problems modeled as an Ising Hamiltonian.

See these installation instructions if you need more information in that regard.

Parameters

operator (WeightedPauliOperator) – The Ising Hamiltonian as an Operator
timelimit (int) – A time limit in seconds for the execution
thread (int) – The number of threads that CPLEX uses. Setting this 0 lets CPLEX decide the number of threads to allocate, but this may not be ideal for small problems for which the default of 1 is more suitable.
display (int) – Decides what CPLEX reports to the screen and records in a log during mixed integer optimization. This value must be between 0 and 5 where the amount of information displayed increases with increasing values of this parameter.

__init__(operator, timelimit=600, thread=1, display=2)[source]¶

Parameters

operator (WeightedPauliOperator) – The Ising Hamiltonian as an Operator
timelimit (int) – A time limit in seconds for the execution
thread (int) – The number of threads that CPLEX uses. Setting this 0 lets CPLEX decide the number of threads to allocate, but this may not be ideal for small problems for which the default of 1 is more suitable.
display (int) – Decides what CPLEX reports to the screen and records in a log during mixed integer optimization. This value must be between 0 and 5 where the amount of information displayed increases with increasing values of this parameter.

Methods

__init__(operator[, timelimit, thread, display])

type operator: WeightedPauliOperator

run()

Execute the classical algorithm.

Attributes

`random`	Return a numpy random.
`solution`	return solution

property random¶: Return a numpy random.

run()¶

Execute the classical algorithm.

Returns: results of an algorithm.
Return type: dict

property solution¶: return solution