應學院邀請，蘭州大學鄭兵教授将來我院作學術報告。
報告題目：A completely self-scaling G-transformation for weighted least square problems
報告摘要：The G-transformation is an efficient method for solving the weighted least squares problems. However, the underflows and overflows were not considered in the original G-transformation. In order to keep its stability, some specified scaling strategies has been proposed for guarding against the underflows. Note that these specific strategies are not easy to be implemented in actual operations, in this talk we present a completely self-scaling G-transformation (CSSGT) which not only avoids these specified scaling strategies, but maintain the stability of operations. Complexity analysis of our self-scaling G-transformation shows that its cost of computation is less than that of the G-transformation, which implies the high efficiency of our proposed SSGT. The stability of the SSGT was theoretically confirmed by a detailed error analysis. Some numerical experiments are performed to illustrate the effects of the self-scaling strategy.
報告時間：2023年10月29日16:30
報告地點：緻勤樓C區101學術報告廳
邀 請 人：孟令勝副教授
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報告人簡介
鄭兵，蘭州大學77779193永利教授，博士生導師。長期從事數值代數、神經網絡算法的研究工作，負責承擔國家自然科學基金面上項目、教育部外國專家重點項目、甘肅省自然科學基金項目等10餘項。多次應邀赴美國、日本、西班牙、俄羅斯、印度以及香港、澳門等國家和地區參加學術會議并做學術報告，并先後在印度統計研究所新德裡中心和美國Emory大學數學與計算機系做訪問學者。迄今已在SIAM J. Matrix Anal. Appl., J. Math. Anal. Appl., J. Optim. Theory Appl., Linear Algebra Appl., J. Multivariate Anal., Adv. Comput. Math., IEEE Trans. Neural Network Learn. Syst., IEEE Trans. Fuzzy Syst., Automatica等國内外重要刊物上發表論文100餘篇。2005年榮獲甘肅省第十二屆高校青年老師成才獎。
甘肅省數學與統計學基礎學科研究中心
77779193永利
2023年10月26日
