solve_SSD_constrained_least_squares_smooth
- pyapprox.optimization.solve_SSD_constrained_least_squares_smooth(samples, values, eval_basis_matrix, eta_indices=None, probabilities=None, eps=0.1, optim_options={}, return_full=False, method='trust-constr', smoother_type='log', scale_data=False)[source]
Solve second order stochastic dominance (SSD) constrained least squares
- Parameters:
- samplesnp.ndarary (nvars, nsamples)
The training samples
- valuesnp.ndarary (nsamples, 1)
The function values at the training samples
- eval_basis_matrixcallable
A function returning the basis evaluated at the set of samples with signature
eval_basis_matrix(samples) -> np.ndarray (nsamples, nbasis)- eta_indicesnp.ndarray (nconstraint_samples)
Indices of the training data at which constraints are enforced neta <= nsamples
- probabilitiesnp.ndarray(nvalues)
The probability weight assigned to each training data. When sampling randomly from a probability measure the probabilities are all 1/nsamples
- epsfloat
A parameter which controls the amount that the heaviside function is smoothed. As eps decreases the smooth approximation converges to the heaviside function but the derivatives of the approximation become more difficult to compute
- optim_optionsdict
The keyword arguments passed to the non-linear optimization used to solve the regression problem
- smoother_typestring
The name of the function used to smooth the heaviside function Supported types are [quartic, quintic, log]
- return_fullboolean
False - return regression solution True - return regression solution and regression optimizer object
- methodstring
The name of the non-linear solver used to solve the regresion problem
- scale_databoolean
False - use raw training values True - scale training values to have unit standard deviation
- Returns:
- coefnp.narray (nbasis, 1)
The solution to the regression problem
- opt_problem
SSDOptProblem The object used to solve the regression problem