Literature Review in International Trade Forecasting Based in Machine Learning Method

In recent years, with the intricacy of international politics and economic situation and the anti-globalization trend, China’s trade with world is facing many serious challenges. There are more factors that effects China export and import. Because of that, high-precision forecasting for international trade is beneficial for nation’s government, guild and export and import enterprise that need a judgement or decision for future. To better promote future research, the paper reviews the paper written by experts from world in trade forecasting field, classifies and summarizes their opinion according in their adopting machine learning method

Multi-consensus Decentralized Accelerated Gradient Descent

This paper considers the decentralized optimization problem, which has applications in large scale machine learning, sensor networks, and control theory. We propose a novel algorithm that can achieve near optimal communication complexity, matching the known lower bound up to a logarithmic factor of the condition number of the problem. Our theoretical results give affirmative answers to the open problem on whether there exists an algorithm that can achieve a communication complexity (nearly) matching the lower bound depending on the global condition number instead of the local one. Moreover, the proposed algorithm achieves the optimal computation complexity matching the lower bound up to universal constants. Furthermore, to achieve a linear convergence rate, our algorithm \emph{doesn't} require the individual functions to be (strongly) convex. Our method relies on a novel combination of known techniques including Nesterov's accelerated gradient descent, multi-consensus and gradient-tracking. The analysis is new, and may be applied to other related problems. Empirical studies demonstrate the effectiveness of our method for machine learning applications