ICNLAMC · Registering as Listener

International Conference on Numerical Linear Algebra and Matrix Computations

📅 15–16 May 2027 📍 Prague, Czech Republic 👤 Standard / Listener

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Supporting global researchConnect with researchers across 30+ countries

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Conference session tracks

Key research areas covered across the sessions — tap a track to read more.

This track focuses on recent developments in the theory and applications of eigenvalue problems. Contributions may include novel algorithms, stability analysis, and case studies demonstrating practical applications.

This session will explore innovative iterative techniques for solving large-scale linear systems. Emphasis will be placed on convergence properties, computational efficiency, and real-world applications.

This track will cover the latest research on direct methods for solving linear systems and matrix equations. Topics may include algorithmic improvements, complexity analysis, and numerical stability considerations.

This session will address the challenges and solutions associated with sparse matrix computations. Contributions are encouraged on efficient storage schemes, factorization methods, and applications in various fields.

This track will delve into preconditioning strategies that enhance the convergence of iterative methods. Discussions will include theoretical foundations, practical implementations, and performance comparisons.

This session will focus on Krylov subspace methods for solving linear systems and eigenvalue problems. Contributions should highlight theoretical advancements, algorithmic innovations, and practical applications.

This track will explore the critical aspects of numerical stability and error bounds in matrix computations. Papers should address both theoretical insights and practical implications in numerical algorithms.

This session will highlight the role of numerical linear algebra in engineering problems. Contributions may include case studies, algorithmic applications, and interdisciplinary collaborations.

This track will cover optimization methods that leverage numerical linear algebra techniques. Topics may include algorithm design, convergence analysis, and applications in various optimization problems.

This session will focus on the implementation of parallel computing strategies in matrix computations. Discussions will include performance metrics, scalability issues, and case studies demonstrating effectiveness.

This track will explore novel applications of numerical methods across diverse fields. Papers should demonstrate the impact of numerical linear algebra on solving real-world problems and advancing research.