optunity.solvers.util module

class optunity.solvers.util.Solver

Bases: abc.SolverBase

Base class of all Optunity solvers.

maximize(f, pmap=<built-in function map>)

Maximizes f.

Parameters:

• f (callable) – the objective function
• pmap (callable) – the map() function to use

Returns:

• the arguments which optimize f
• an optional solver report, can be None

minimize(f, pmap=<built-in function map>)

Minimizes f.

Parameters:

• f (callable) – the objective function
• pmap (callable) – the map() function to use

Returns:

• the arguments which optimize f
• an optional solver report, can be None

optimize(f, maximize=True, pmap=<built-in function map>)

Optimizes f.

Parameters:

• f (callable) – the objective function
• maximize (boolean) – do we want to maximizes?
• pmap (callable) – the map() function to use

Returns:

• the arguments which optimize f
• an optional solver report, can be None

optunity.solvers.util.shrink_bounds(bounds, coverage=0.99)

Shrinks the bounds. The new bounds will cover the fraction coverage.

>>> [round(x, 3) for x in shrink_bounds([0, 1], coverage=0.99)]
[0.005, 0.995]

optunity.solvers.util.uniform_in_bounds(bounds)

Generates a random uniform sample between bounds.

Parameters:bounds (dict {“name”: [lb ub], ...}) – the bounds we must adhere to