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Serveless-based High-Dimensional Matrix Operations and Their Financial Applications
- 1 School of Telecommunications Engineering, Xidian University, Xi'an City, China
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Abstract
This paper systematically analyzes multivariate methods for high-dimensional matrix computation and their optimization strategies for applications in finance. At the level of high-dimensional computation, it focuses on the technical characteristics of direct methods, iterative methods , and randomized algorithms , which reveal their efficiency gains in financial derivatives pricing, risk matrix modeling, and other scenarios. For serverless architecture, the study focuses on its core advantages of elastic scaling and on-demand billing, through parallel task slicing and cost optimization, while analyzing the limitations of its stateless design on the adaptation of iterative algorithms and the constraints of cold-start latency on high-frequency trading. In addition, the article delves into the special challenges of financial modeling, including the cubic complexity pressure of high-dimensional operations, real-time conflicts of missing data interpolation, and privacy compliance requirements, and discusses hybrid architectures (serverless with local GPU synergy) and middleware (Redis, AWS Step Functions) as the current transitional solutions for balancing efficiency and state. The research also addresses the challenges of nonlinear dynamic modeling and interpretability requirements for machine learning-driven models, providing a multidimensional analytical framework for technology adaptability.
Keywords
Serveless, High-Dimensional Matrix, Financial Applications
[1]. Dean, J., Ghemawat, S.: 'MapReduce: Simplified Data Processing on Large Clusters’, Commun. ACM, 2008, 51, (1), pp. 107–113
[2]. Amazon Web Services (AWS): 'AWS Lambda: A Serverless Compute Service’, AWS Whitepaper, 2020
[3]. Strang, G.: 'Introduction to Linear Algebra’, 5th edn., Wellesley-Cambridge Press, 2016
[4]. Halko, N., Martinsson, P.G., Tropp, J.A.: 'Finding Structure with Randomness: Probabilistic Algorithms for Constructing Approximate Matrix Decompositions’, SIAM Rev., 2011, 53, (2), pp. 217–288
[5]. Duff, I.S., Erisman, A.M., Reid, J.K.: 'Direct Methods for Sparse Matrices’, Oxford Univ. Press, 2017
[6]. AWS Case Study: 'High-Performance Matrix Computation on AWS Lambda’, AWS Comput. Blog, 2022
[7]. J.P. Morgan Chase & Co.: 'Hybrid Serverless Architecture for Financial Risk Modeling’, Tech. Rep., 2023
[8]. AWS Documentation: 'Reducing Cold Starts with AWS Lambda SnapStart’, AWS Developer Guide, 2023
[9]. Smith, R.: 'Accelerating Financial Simulations with Serverless Computing’, J. Financ. Eng., 2021, 18, (3), pp. 45–60
[10]. Kairouz, P., et al.: 'Advances and Open Problems in Federated Learning’, arXiv preprint arXiv:1912.04977, 2019
Cite this article
Ying,S. (2025). Serveless-based High-Dimensional Matrix Operations and Their Financial Applications. Applied and Computational Engineering,158,11-16.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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Volume title: Proceedings of CONF-SEML 2025 Symposium: Machine Learning Theory and Applications
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