torchmin.minimize_constr

torchmin.minimize_constr(f, x0, constr=None, bounds=None, max_iter=None, tol=None, callback=None, disp=0, **kwargs)[source]

Minimize a scalar function of one or more variables subject to bounds and/or constraints.

Note

This is a wrapper for SciPy’s ‘trust-constr’ method. It uses autograd behind the scenes to build jacobian & hessian callables before invoking scipy. Inputs and objectivs should use PyTorch tensors like other routines. CUDA is supported; however, data will be transferred back-and-forth between GPU/CPU.

Parameters

  • f (callable) – Scalar objective function to minimize.

  • x0 (Tensor) – Initialization point.

  • constr (dict, optional) –

    Constraint specifications. Should be a dictionary with the following fields:

    • fun (callable) - Constraint function

    • lb (Tensor or float, optional) - Constraint lower bounds

    • ub : (Tensor or float, optional) - Constraint upper bounds

    One of either lb or ub must be provided. When lb == ub it is interpreted as an equality constraint.

  • bounds (dict, optional) –

    Bounds on variables. Should a dictionary with at least one of the following fields:

    • lb (Tensor or float) - Lower bounds

    • ub (Tensor or float) - Upper bounds

    Bounds of -inf/inf are interpreted as no bound. When lb == ub it is interpreted as an equality constraint.

  • max_iter (int, optional) – Maximum number of iterations to perform. If unspecified, this will be set to the default of the selected method.

  • tol (float, optional) – Tolerance for termination. For detailed control, use solver-specific options.

  • callback (callable, optional) – Function to call after each iteration with the current parameter state, e.g. callback(x).

  • disp (int) –

    Level of algorithm’s verbosity:

    • 0 : work silently (default).

    • 1 : display a termination report.

    • 2 : display progress during iterations.

    • 3 : display progress during iterations (more complete report).

  • **kwargs – Additional keyword arguments passed to SciPy’s trust-constr solver. See options here.

Returns

result – Result of the optimization routine.

Return type

OptimizeResult