大規模分解機器之最佳化方法比較

Abstract

近年來，非凸最佳化問題變得相當熱門。非凸最佳化問題是個充滿許多未知數的領域，也非常值得花費力氣去研究最佳化方法在這類問題上的行為模式。此外，分解機器也漸漸廣泛地被使用在各類型的應用上面，特別是推薦系統。為了深入了解，我們分析了交替牛頓法以及常見方向在分解機器上的行為。本作品的主要貢獻是：詳細的比較了模型之間的相對目標函式值、訓練時間、偽數據遍歷。實驗結果顯示交替常見方向在收斂速度上較交替牛頓法來的快。

Recently, non-convex optimization has been a popular domain. Non-convex optimization is a domain full of unknowns and it is worth investigating behaviors of optimzation techniques on such kind of problems. Also, Factorization Machine has also been a popular model in many applications, especially for recommendation systems. To know the details, we analyze the behaviors of alternating Newton method (ANT) and alternating common-directions method on the model. In this work, we compare their relative objective function value, training time, and pseudo data passe.