Welcome to anneal’s documentation!¶

Here I say something.

Contents¶

Introduction¶

real introduction

Simulated annealing¶

link

Method/problem decoupling

How to install¶

Note

This is a note admonition, concerning versions and requirements. This is the second line of the first paragraph.

pip¶

pip with version

github¶

github with development version

just try¶

try it out, simple enough

Usage¶

how to use

anneal¶

anneal.simulated_annealing(P, ID, beta_min=0.01, beta_max=100.0, cooling_rate=0.01, n_steps_per_T=100, E_min=-inf, quench_to_T0=False, n_steps_T0=1000)[source]¶

General-purpose simulated-annealing optimization function.

Parameters:

P : object

Instance of a custom class, wich includes attributes

P.beta

P.energy

and methods:

P.set_beta(beta)

P.MC_move(), returning 1/0 (accepted/rejected)

P.update_MC_parameters(acc_ratio)

ID : str

Label for the problem under study.

beta_min : float, optional

Minimum inverse temperature (default: 1e-2)

beta_max: float, optional

Maximum inverse temperature (default: 1e2)

cooling_rate : float, optional

Cooling rate (default: 1e-2)

n_steps_per_T : int, optional

Number of MC moves attempted at each temperature (default: 100)

E_min : float, optional

Global energy minimum, if known (default: -infinity)

quench_to_T0 : bool, optional

If True, perform a T=0 quench at the end of the annealing

n_steps_T0 : int, optional

Number of MC moves after the T=0 quench

Returns:

P : object

Current version of P

E : list

List of the final energies for each temperature

elapsed_time : float

Total elapsed time, in seconds