Randomized Numerical Linear Algebra - Overview
Explore randomized numerical linear algebra techniques for efficient matrix computations, covering sampling methods, error bounds, and applications to least-squares problems and SVD.
Advanced Techniques for Low-Rank Matrix Approximation
Explore cutting-edge methods for low-rank matrix approximation, including randomized algorithms and factorizations, with insights on efficiency and accuracy comparisons.
Efficient Quantum Algorithm for Dissipative Nonlinear Differential Equations
Explore efficient quantum algorithms for nonlinear differential equations, overcoming limitations through Carleman linearization. Discover potential applications in biology, fluid dynamics, and plasma physics.
Numerical Stability of Algorithms at Extreme Scale and Low Precisions
Explore numerical stability in extreme-scale computing and low-precision arithmetic, focusing on error bounds, blocked algorithms, and probabilistic analysis for matrix computations.
Factorization-based Sparse Solvers and Preconditioners - Lecture 3
Explore factorization-based sparse solvers and preconditioners, covering topics like minimum degree ordering, numerical pivoting, and sparse Gaussian elimination techniques.
ME3255 - Linear Algebra, Condition Numbers, and Error
Explore linear algebra concepts, condition numbers, and error analysis in numerical methods. Enhance problem-solving skills for engineering applications.
Convergence Analysis and Parameter Choice for the Iterated Arnoldi-Tikhonov Method
Explore convergence analysis and parameter selection for the iterated Arnoldi-Tikhonov method in this mathematical conference talk by Lothar Reichel.
Quantum Approximate Optimization Algorithm and Local Max-Cut - IPAM at UCLA
Explore quantum optimization algorithms, focusing on QAOA's application to LocalMaxCut. Discover insights on quantum supremacy and the performance comparison between local algorithms and QAOA on various graph instances.
Heisenberg-Limited Ground State Energy Estimation for Early Fault-Tolerant Quantum Computers
Explore Heisenberg-limited ground state energy estimation for early fault-tolerant quantum computers, featuring a simplified circuit and classical post-processing for improved efficiency.
Optimal Scaling Quantum Linear Systems Solver via Discrete Adiabatic Theorem
Explore optimal quantum linear systems solver using discrete adiabatic theorem, achieving linear complexity in condition number and precision, with simplified implementation and constant factor analysis.
The Quantum Linear Systems Problem - IPAM at UCLA
Explore quantum algorithms for solving linear systems, including improved methods and complexity analysis, with a focus on potential exponential speedups and verification challenges.
Fast Algorithms for Quantum Signal Processing - IPAM at UCLA
Explore fast algorithms for quantum signal processing, focusing on optimization techniques, energy landscapes, and phase factor structures in quantum singular value transformation.
Time-Dependent Hamiltonian Simulation of Highly Oscillatory Dynamics - IPAM at UCLA
Explore highly oscillatory dynamics in quantum systems, focusing on efficient simulation algorithms for time-dependent Hamiltonians with applications in various scientific fields.
Hierarchical Interpolative Factorization
Explore efficient matrix factorization techniques for differential and integral operators, focusing on interpolative decomposition and its applications in various dimensions and problem types.
When Data Driven Reduced Order Modeling Meets Full Waveform Inversion - Rothschild Lecture
Explore innovative approaches to velocity estimation in wave equation inverse problems, focusing on data-driven reduced order modeling and its application to full waveform inversion.
Gauss-Legendre Features for Scalable Gaussian Process Regression
Discover how Gauss-Legendre features enable efficient scaling of Gaussian process regression through low-rank kernel matrix approximation, offering faster computation for large datasets with poly-logarithmic feature requirements.