def_module_params('sls', export=True, description='Experimental Stochastic Local Search Solver (for QFBV only).', params=(max_memory_param(), ('max_restarts', UINT, UINT_MAX, 'maximum number of restarts'), ('walksat', BOOL, 1, 'use walksat assertion selection (instead of gsat)'), ('walksat_ucb', BOOL, 1, 'use bandit heuristic for walksat assertion selection (instead of random)'), ('walksat_ucb_constant', DOUBLE, 20.0, 'the ucb constant c in the term score + c * f(touched)'), ('walksat_ucb_init', BOOL, 0, 'initialize total ucb touched to formula size'), ('walksat_ucb_forget', DOUBLE, 1.0, 'scale touched by this factor every base restart interval'), ('walksat_ucb_noise', DOUBLE, 0.0002, 'add noise 0 <= 256 * ucb_noise to ucb score for assertion selection'), ('walksat_repick', BOOL, 1, 'repick assertion if randomizing in local minima'), ('scale_unsat', DOUBLE, 0.5, 'scale score of unsat expressions by this factor'), ('paws_init', UINT, 40, 'initial/minimum assertion weights'), ('paws_sp', UINT, 52, 'smooth assertion weights with probability paws_sp / 1024'), ('wp', UINT, 100, 'random walk with probability wp / 1024'), ('vns_mc', UINT, 0, 'in local minima, try Monte Carlo sampling vns_mc many 2-bit-flips per bit'), ('vns_repick', BOOL, 0, 'in local minima, try picking a different assertion (only for walksat)'), ('restart_base', UINT, 100, 'base restart interval given by moves per run'), ('restart_init', BOOL, 0, 'initialize to 0 or random value (= 1) after restart'), ('early_prune', BOOL, 1, 'use early pruning for score prediction'), ('random_offset', BOOL, 1, 'use random offset for candidate evaluation'), ('rescore', BOOL, 1, 'rescore/normalize top-level score every base restart interval'), ('track_unsat', BOOL, 0, 'keep a list of unsat assertions as done in SAT - currently disabled internally'), ('random_seed', UINT, 0, 'random seed') ))