论文标题
综合目标的乐观优化，并具有指定更新
Optimistic Optimisation of Composite Objective with Exponentiated Update
论文作者
论文摘要
本文提出了一个新的算法系列，用于在线优化复合目标。这些算法可以解释为凸起梯度和$ p $ - 纳米算法的组合。结合适应性和乐观的算法思想，所提出的算法获得了序列依赖的遗憾上限，与稀疏目标决策变量的最著名界限相匹配。此外，该算法具有对流行的复合目标和约束的有效实现，并且可以通过最佳的加速速率转换为随机优化算法，以实现平滑目标。
This paper proposes a new family of algorithms for the online optimisation of composite objectives. The algorithms can be interpreted as the combination of the exponentiated gradient and $p$-norm algorithm. Combined with algorithmic ideas of adaptivity and optimism, the proposed algorithms achieve a sequence-dependent regret upper bound, matching the best-known bounds for sparse target decision variables. Furthermore, the algorithms have efficient implementations for popular composite objectives and constraints and can be converted to stochastic optimisation algorithms with the optimal accelerated rate for smooth objectives.
