218 lines
6.4 KiB
Python
218 lines
6.4 KiB
Python
import nlopt
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import numpy as np
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NLOPTIMIZERS = {
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# "gd_mlsl": nlopt.GD_MLSL,
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# "gd_mlsl_lds": nlopt.GD_MLSL_LDS,
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# "gd_stogo": nlopt.GD_STOGO,
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# "gd_stogo_rand": nlopt.GD_STOGO_RAND,
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"gn_ags": nlopt.GN_AGS,
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"gn_crs2_lm": nlopt.GN_CRS2_LM,
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"gn_direct": nlopt.GN_DIRECT,
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"gn_direct_l": nlopt.GN_DIRECT_L,
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"gn_direct_l_noscal": nlopt.GN_DIRECT_L_NOSCAL,
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"gn_direct_l_rand": nlopt.GN_DIRECT_L_RAND,
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"gn_direct_l_rand_noscal": nlopt.GN_DIRECT_L_RAND_NOSCAL,
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"gn_direct_noscal": nlopt.GN_DIRECT_NOSCAL,
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"gn_esch": nlopt.GN_ESCH,
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"gn_isres": nlopt.GN_ISRES,
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"gn_mlsl": nlopt.GN_MLSL,
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"gn_mlsl_lds": nlopt.GN_MLSL_LDS,
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"gn_orig_direct": nlopt.GN_ORIG_DIRECT,
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"gn_orig_direct_l": nlopt.GN_ORIG_DIRECT_L,
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"g_mlsl": nlopt.G_MLSL,
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"g_mlsl_lds": nlopt.G_MLSL_LDS,
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# "ld_auglag": nlopt.LD_AUGLAG,
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# "ld_auglag_eq": nlopt.LD_AUGLAG_EQ,
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# "ld_ccsaq": nlopt.LD_CCSAQ,
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# "ld_lbfgs": nlopt.LD_LBFGS,
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# "ld_lbfgs_nocedal": nlopt.LD_LBFGS_NOCEDAL,
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# "ld_mma": nlopt.LD_MMA,
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# "ld_slsqp": nlopt.LD_SLSQP,
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# "ld_tnewton": nlopt.LD_TNEWTON,
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# "ld_tnewton_precond": nlopt.LD_TNEWTON_PRECOND,
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# "ld_tnewton_precond_restart": nlopt.LD_TNEWTON_PRECOND_RESTART,
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# "ld_tnewton_restart": nlopt.LD_TNEWTON_RESTART,
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# "ld_var1": nlopt.LD_VAR1,
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# "ld_var2": nlopt.LD_VAR2,
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"ln_auglag": nlopt.LN_AUGLAG,
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"ln_auglag_eq": nlopt.LN_AUGLAG_EQ,
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"ln_bobyqa": nlopt.LN_BOBYQA,
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"ln_cobyla": nlopt.LN_COBYLA,
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"ln_neldermead": nlopt.LN_NELDERMEAD,
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"ln_newuoa": nlopt.LN_NEWUOA,
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"ln_newuoa_bound": nlopt.LN_NEWUOA_BOUND,
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"ln_praxis": nlopt.LN_PRAXIS,
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"ln_sbplx": nlopt.LN_SBPLX,
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}
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def nlopt_cube_factory(objective, size, budget, method):
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optim = NLOPTIMIZERS[method]
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feval_count = 0
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best_so_far = None
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def _objective(x, grad):
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nonlocal feval_count, best_so_far
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fx = objective(x)
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feval_count += 1
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if feval_count <= budget:
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if best_so_far is None or fx < best_so_far[0]:
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best_so_far = (fx, x)
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return fx
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opt = nlopt.opt(optim, size)
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opt.set_lower_bounds([0.0] * size)
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opt.set_upper_bounds([1.0] * size)
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opt.set_min_objective(_objective)
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opt.set_maxeval(budget)
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try:
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opt.optimize([0.5] * size)
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except nlopt.RoundoffLimited as e:
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print( # FIXME: de-uglify this!
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"\033[33m",
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"nlopt_" + method,
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" exited early (",
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type(e).__name__,
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")",
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"\033[0m",
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sep="",
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)
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assert best_so_far is not None, optimizer.__name__
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fopt, xopt = best_so_far
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return fopt, xopt, feval_count
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def nlopt_ags_cube(objective, size, budget):
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return nlopt_cube_factory(objective, size, budget, "gn_ags")
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def nlopt_crs2_lm_cube(objective, size, budget):
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return nlopt_cube_factory(objective, size, budget, "gn_crs2_lm")
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def nlopt_direct_cube(objective, size, budget):
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return nlopt_cube_factory(objective, size, budget, "gn_direct")
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def nlopt_direct_l_cube(objective, size, budget):
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return nlopt_cube_factory(objective, size, budget, "gn_direct_l")
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def nlopt_direct_l_noscal_cube(objective, size, budget):
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return nlopt_cube_factory(
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objective, size, budget, "gn_direct_l_noscal"
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)
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def nlopt_direct_lr_cube(objective, size, budget):
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return nlopt_cube_factory(
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objective, size, budget, "gn_direct_l_rand"
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)
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def nlopt_direct_lr_noscal_cube(objective, size, budget):
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return nlopt_cube_factory(
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objective, size, budget, "gn_direct_l_rand_noscal"
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)
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def nlopt_direct_noscal_cube(objective, size, budget):
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return nlopt_cube_factory(
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objective, size, budget, "gn_direct_noscal"
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)
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def nlopt_esch_cube(objective, size, budget):
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return nlopt_cube_factory(objective, size, budget, "gn_esch")
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def nlopt_isres_cube(objective, size, budget):
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return nlopt_cube_factory(objective, size, budget, "gn_isres")
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def nlopt_mlsl_cube(objective, size, budget):
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return nlopt_cube_factory(objective, size, budget, "gn_mlsl")
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def nlopt_mlsl_lds_cube(objective, size, budget):
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return nlopt_cube_factory(objective, size, budget, "gn_mlsl_lds")
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def nlopt_orig_direct_cube(objective, size, budget):
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return nlopt_cube_factory(objective, size, budget, "gn_orig_direct")
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def nlopt_orig_direct_l_cube(objective, size, budget):
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return nlopt_cube_factory(
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objective, size, budget, "gn_orig_direct_l"
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)
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def nlopt_auglag_cube(objective, size, budget):
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return nlopt_cube_factory(objective, size, budget, "ln_auglag")
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def nlopt_auglag_eq_cube(objective, size, budget):
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return nlopt_cube_factory(objective, size, budget, "ln_auglag_eq")
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def nlopt_bobyqa_cube(objective, size, budget):
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return nlopt_cube_factory(objective, size, budget, "ln_bobyqa")
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def nlopt_cobyla_cube(objective, size, budget):
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return nlopt_cube_factory(objective, size, budget, "ln_cobyla")
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def nlopt_neldermead_cube(objective, size, budget):
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return nlopt_cube_factory(objective, size, budget, "ln_neldermead")
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def nlopt_newuoa_cube(objective, size, budget):
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return nlopt_cube_factory(objective, size, budget, "ln_newuoa")
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def nlopt_newuoa_bound_cube(objective, size, budget):
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return nlopt_cube_factory(objective, size, budget, "ln_newuoa_bound")
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def nlopt_praxis_cube(objective, size, budget):
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return nlopt_cube_factory(objective, size, budget, "ln_praxis")
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def nlopt_sbplx_cube(objective, size, budget):
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return nlopt_cube_factory(objective, size, budget, "ln_sbplx")
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NLOPT_OPTIMIZERS = [
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# global
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# nlopt_ags_cube, # ValueError: nlopt invalid argument
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nlopt_crs2_lm_cube,
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nlopt_direct_cube,
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nlopt_direct_l_cube,
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nlopt_direct_l_noscal_cube,
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nlopt_direct_lr_cube,
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nlopt_direct_lr_noscal_cube,
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nlopt_direct_noscal_cube,
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nlopt_esch_cube,
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nlopt_isres_cube,
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# nlopt_mlsl_cube, # FIXME: Segmentation fault
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# nlopt_mlsl_lds_cube # FIXME: Segmentation fault,
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nlopt_orig_direct_cube,
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nlopt_orig_direct_l_cube,
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# local
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# nlopt_auglag_cube, # FIXME: Segmentation fault
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# nlopt_auglag_eq_cube, # FIXME: Segmentation fault
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nlopt_bobyqa_cube,
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nlopt_cobyla_cube,
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nlopt_neldermead_cube,
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nlopt_newuoa_cube,
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# nlopt_newuoa_bound_cube, # sadly this times out on shekel. however it wins in nlopt_classic_d21_n550_armor
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# nlopt_praxis_cube, # AssertionError: x is not finite (NaN or Inf or -Inf)
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nlopt_sbplx_cube,
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]
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