论文标题
$ 1D $至$ nd $：通过单变量优化器进行多元全局优化的元算法
$1D$ to $nD$: A Meta Algorithm for Multivariate Global Optimization via Univariate Optimizers
论文作者
论文摘要
在这项工作中，我们提出了一种可以使用单变量全局优化器来解决多元全局优化问题的元算法。尽管与多元案例相比，单变量全球优化并没有得到太多关注，这在学术界和工业中更加强调。我们表明它仍然是相关的，可以直接用于解决多元优化的问题。我们还提供了相应的遗憾界限，并在具有强大的遗憾保证的情况下对非负噪声的强大时，就可以为单变量优化器的平均遗憾和单变量优化的平均遗憾提供。
In this work, we propose a meta algorithm that can solve a multivariate global optimization problem using univariate global optimizers. Although the univariate global optimization does not receive much attention compared to the multivariate case, which is more emphasized in academia and industry; we show that it is still relevant and can be directly used to solve problems of multivariate optimization. We also provide the corresponding regret bounds in terms of the time horizon $T$ and the average regret of the univariate optimizer, when it is robust against nonnegative noises with robust regret guarantees.
