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我将 [LikelihoodProfiler]: https://github.com/insysbio/LikelihoodProfiler.jl从 Julia 重写为 Python。我需要为[非线性约束]编写约束函数： https ://nlopt.readthedocs.io/en/latest/NLopt_Python_Reference/#nonlinear-constraints ，就好像我们得到一些我们抛出的值[强制停止异常]：https://nlopt .readthedocs.io/en/latest/NLopt_Python_Reference/# exc Nlopt 必须处理异常并使用特殊代码返回结果。

在 [Julia 它看起来]：https ://github.com/insysbio/LikelihoodProfiler.jl/blob/master/src/cico_one_pass.jl

function constraints_func(x, g)
        loss = loss_func(x)
        if (loss < 0.) && (scan_func(x) > scan_bound)
            throw(ForcedStop("Out of the scan bound but in ll constraint."))
        #elseif isapprox(loss, 0., atol=loss_tol)
        #    @warn "loss_tol reached... but..."
        #    return loss
        else
            return loss
        end
    end

 opt = Opt(:LN_AUGLAG, n_theta)
    ftol_abs!(opt, scan_tol)
    max_objective!(
        opt,
        (x, g) -> scan_func(x)
        )
    lb = [theta_bounds[i][1] for i in 1:n_theta] # minimum.(theta_bounds)
    ub = [theta_bounds[i][2] for i in 1:n_theta] # maximum.(theta_bounds)
    lower_bounds!(opt, lb)
    upper_bounds!(opt, ub)
    local_optimizer!(opt, local_opt)
    maxeval!(opt, max_iter)

    # inequality constraints
    inequality_constraint!(
        opt,
        constraints_func,
        loss_tol
    )

    # start optimization
    (optf, optx, ret) = optimize(opt, theta_init)

我尝试将其重写为 python：

 # Constraints function

    def constraints_func(x, g, opt):
        loss = loss_func(x)
        if (loss < 0) and (scan_func(x) > scan_bound):
            opt.force_stop()
            #raise nlopt.ForcedStop("Out of the scan bound but in ll constraint.")
        else:
            return loss

    # constrain optimizer
    opt = nlopt.opt(nlopt.LN_AUGLAG, n_theta)
    opt.set_ftol_abs(scan_tol)
    opt.set_max_objective(lambda x, g: scan_func(x))

    lb = [theta_bounds[i][0] for i in range(n_theta)]  # minimum.(theta_bounds)
    ub = [theta_bounds[i][1] for i in range(n_theta)]  # maximum.(theta_bounds)
    opt.set_lower_bounds(lb)
    opt.set_upper_bounds(ub)
    opt.set_local_optimizer(local_opt)
    opt.set_maxeval(max_iter)
    # print(max_iter)
    # inequality constraints
    opt.add_inequality_constraint(lambda x, g: constraints_func(x, g, opt), loss_tol)
    # start optimization
    optx = opt.optimize(theta_init)
    optf = opt.last_optimum_value()
    ret = opt.last_optimize_result()

但是当我运行它时，我得到nlopt invalid argument，如果相反

opt.force_stop()

我用

raise nlopt.ForcedStop("Out of the scan bound but in ll constraint.")

我得到nlopt.ForcedStop: Out of the scan bound but in ll 约束

但我预计，Nlopt 会使用特殊代码处理异常并返回优化结果。

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1 回答 1

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Unfochently，我无法解决这个问题，但我使用标准的python方法

   try:
        optx = opt.optimize(theta_init)
        optf = opt.last_optimum_value()
        ret = opt.last_optimize_result()
    except nlopt.ForcedStop:
        ret = -5

def constraints_func(x, g):
        loss = loss_func(x)
        if (loss < 0) and (scan_func(x) > scan_bound):
            #return opt.force_stop()
            raise nlopt.ForcedStop("Out of the scan bound but in ll constraint.")
        else:
            return loss
于 2019-07-30T11:23:57.167 回答