{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T23:36:57Z","timestamp":1761176217808,"version":"build-2065373602"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643686318","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,10,21]],"date-time":"2025-10-21T00:00:00Z","timestamp":1761004800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,10,21]]},"abstract":"<jats:p>In this paper, we introduce the Perturbed Natural Adaptive Gradient Descent (PN-AdaGrad) method, a novel optimization algorithm that combines the principles of Natural gradient descent and adaptive gradient descent on Riemannian manifold. We provide a rigorous theoretical analysis of the PN-AdaGrad method, proving its convergence to critical point of the objective function under mild assumptions. To validate the practical effectiveness of the PN-AdaGrad method, we verify our algorithm on real-world datasets in the context of portfolio optimization. Portfolio optimization involves selecting the optimal allocation of assets to maximize returns while minimizing risk. Our experiments show that the PN-AdaGrad method outperforms traditional gradient descent and other state-of-the-art optimization algorithms.<\/jats:p>","DOI":"10.3233\/faia251131","type":"book-chapter","created":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T09:52:24Z","timestamp":1761126744000},"source":"Crossref","is-referenced-by-count":0,"title":["On Perturbed Natural Adaptive Gradient Descent and Its Application in Portfolio Optimization"],"prefix":"10.3233","author":[{"given":"Yi","family":"Cai","sequence":"first","affiliation":[{"name":"Shanghai University of Finance and Economics"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Huili","family":"Liang","sequence":"additional","affiliation":[{"name":"Shanghai University of Finance and Economics"},{"name":"MoE Key Laboratory of Interdisciplinary Research of Computation and Economics"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yue","family":"Qiu","sequence":"additional","affiliation":[{"name":"Shanghai University of International Business and Economics"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiao","family":"Wang","sequence":"additional","affiliation":[{"name":"Shanghai University of Finance and Economics"},{"name":"MoE Key Laboratory of Interdisciplinary Research of Computation and Economics"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tian","family":"Xie","sequence":"additional","affiliation":[{"name":"Shanghai University of Finance and Economics"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zixuan","family":"Zhao","sequence":"additional","affiliation":[{"name":"Shanghai University of Finance and Economics"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","ECAI 2025"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA251131","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T09:52:25Z","timestamp":1761126745000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA251131"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,21]]},"ISBN":["9781643686318"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia251131","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,21]]}}}