Shape restricted statistical estimation problems have been extensively studied, with many important practical applications in signal processing, bioinformatics, and machine learning. In this paper, we propose and study a generalized nearly isotonic optimization (GNIO) model, which recovers, as special cases, many classic problems in shape constrained statistical regression, such as isotonic regression, nearly isotonic regression and unimodal regression problems. We develop an efficient and easy-to-implement dynamic programming algorithm for solving the proposed model whose recursion nature is carefully uncovered and exploited. For special $\ell_2$-GNIO problems, implementation details and the optimal ${\cal O}(n)$ running time analysis of our algorithm are discussed. Numerical experiments, including the comparison among our approach, the powerful commercial solver Gurobi, and existing fast algorithms for solving $\ell_1$-GNIO and $\ell_2$-GNIO problems, on both simulated and real data sets, are presented to demonstrate the high efficiency and robustness of our proposed algorithm in solving large scale GNIO problems.
翻译:已经广泛研究了限制统计估计的形状问题,在信号处理、生物信息学和机器学习方面有许多重要的实际应用,我们在本文件中提议并研究一个普遍化的近等离子优化模型,作为特殊案例,该模型恢复了许多典型的统计回归障碍问题,如等离子回归、近等离子回归和单式回归问题。我们开发了高效和易于实施的动态编程算法,以解决反复性质被仔细发现和利用的拟议模型。对于特殊值为2美元GNIO问题、实施细节和我们算法的最佳运行时间分析$=0.O(n),我们讨论了数值实验,包括我们的方法、强大的商业求解器Grobi之间的比较,以及目前用于解决$/ell_1美元GNIO和$\ell_2美元-GNIO问题的模拟和真实数据集快速算法,以显示我们拟议的算法在解决大规模GNIO问题方面的效率和稳健性。