摘要
由一名新闻记者-机器人与机器学习每日新闻的工作人员新闻编辑-关于机器学习的最新研究结果已经发表。根据NewsRx Edito RS在越南河内的新闻报道，研究表明：“为了解决无界约束集上的一类广泛的非凸规划问题，我们提供了一种不包括线搜索技术的自适应步长策略，并在温和的假设下建立了一般方法的收敛性。具体地说，目标函数可能不满足凸性条件。”这项研究的资助者包括Tryyng Dstrok;Yi Hyc Bch Khoa H Nyi,河内科技大学(HUST)。我们的新闻记者从河内科技大学的研究中获得了一句话：“与下降线搜索算法不同，它不需要已知的Lipschitz常数就能算出第一步应该有多大。这个过程的关键特征是在满足一定条件之前，步长稳步减小。特别是，"它为求解无界约束优化问题提供了一种新的梯度投影方法."
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-Current study results on Machine Learning have be en published. According to news reporting out of Hanoi, Vietnam, by NewsRx edito rs, research stated, "For solving a broad class of nonconvex programming problem s on an unbounded constraint set, we provide a self-adaptive step-size strategy that does not include line-search techniques and establishes the convergence of a generic approach under mild assumptions. Specifically, the objective function may not satisfy the convexity condition." Funders for this research include Tryyng Dstrok;yi hyc Bch Khoa H Nyi, Hanoi Uni versity of Science and Technology (HUST). Our news journalists obtained a quote from the research from the Hanoi Universit y of Science and Technology, "Unlike descent line-search algorithms, it does not need a known Lipschitz constant to figure out how big the first step should be. The crucial feature of this process is the steady reduction of the step size un til a certain condition is fulfilled. In particular, it can provide a new gradie nt projection approach to optimization problems with an unbounded constrained se t."
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