Source code for nubo.optimisation.singlepoint

import torch
from torch import Tensor
from typing import Tuple, Optional, Callable, Any
from nubo.optimisation import lbfgsb, slsqp, adam, mixed

[docs] def single(func: Callable, method: str, bounds: Tensor, constraints: Optional[dict | list]=None, discrete: Optional[dict]=None, lr: Optional[float]=0.1, steps: Optional[int]=100, num_starts: Optional[int]=10, num_samples: Optional[int]=100, **kwargs: Any) -> Tuple[Tensor, Tensor]: """ Single-point optimisation. Optimises the acquisition function with the L-BFGS-B, SLSQP, or Adam optimiser. Minimises `func`. Parameters ---------- func : ``Callable`` Function to optimise. method : ``str`` One of "L-BFGS-B", "SLSQP", or "Adam". bounds : ``torch.Tensor`` (size 2 x d) Optimisation bounds of input space. constraints : ``dict`` or ``list`` of ``dict``, optional Optimisation constraints. discrete : ``dict`` Possible values for all discrete inputs in the shape {dim1: [values1], dim2: [values2], etc.}, e.g. {0: [1., 2., 3.], 3: [-0.1, -0.2, 100.]}. lr : ``float``, optional Learning rate of ``torch.optim.Adam`` algorithm, default is 0.1. steps : ``int``, optional Optimisation steps of ``torch.optim.Adam`` algorithm, default is 200. num_starts : ``int``, optional Number of start for multi-start optimisation, default is 10. num_samples : ``int``, optional Number of samples from which to draw the starts, default is 100. **kwargs : ``Any`` Keyword argument passed to ``torch.optim.Adam`` or ``scipy.optimize.minimze``. Returns ------- best_result : ``torch.Tensor`` (size 1 x d) Minimiser inputs. best_func_result : ``torch.Tensor`` (size 1) Minimiser output. """ if method not in ["L-BFGS-B", "SLSQP", "Adam"]: raise NotImplementedError("Method must be one of L-BFGS-B, SLSQP or Adam.") # continuous parameter space if not isinstance(discrete, dict): if method == "L-BFGS-B": result, func_result = lbfgsb(func, bounds, num_starts, num_samples, **kwargs) elif method == "SLSQP": result, func_result = slsqp(func, bounds, constraints, num_starts, num_samples, **kwargs) elif method == "Adam": result, func_result = adam(func, bounds, lr, steps, num_starts, num_samples, **kwargs) # mixed parameter space else: result, func_result = mixed(func, method, bounds, discrete, constraints, lr, steps, num_starts, num_samples, **kwargs) return result, func_result