JMM 2023 Special Session: Perspectives on Eigenvalue Computation

The official schedule is posted at https://www.jointmathematicsmeetings.org/meetings/national/jmm2023/2270_program_ss94.html#title

The Zoom link for virtual attendance/virtual talks is:
https://berkeley.zoom.us/j/94982941411?pwd=RWRXeVdoZUNVTmdqK1FIMksrZFllZz09

Friday, January 6 (101 Hynes Convention Center)

1-2pm: Matt Colbrook, The foundations of infinite-dimensional spectral computations (Zoom)

2-3pm: Josué Tonelli-Cueto, Condition-based Low-Degree Approximation of Real Polynomial Systems

3-4pm: Open Problem Session

4-5pm: "Discussion 1": Santosh Vempala, Eigenvalue Computation and Roots of Blackbox Polynomials (abstract below)

We give a simple algorithm and analysis for finding the largest root of a real-rooted polynomial, where one can only query the value of the polynomial at desired inputs. We show that an "accelerated" Newton iteration needs only O(log n log(1/\epsilon)) queries to approximate the larges root of a degree n polynomial within additive error \epsilon. This implies a nearly matrix multiplication complexity algorithm for computing the largest eigenvalue of an (explicit) symmetric matrix to within additive error \epsilon, which, as far as we know, remains the state-of-the-art.
(Joint work with Anand Louis).

Saturday, January 7 (102 Hynes Convention Center)

1-2pm: Agnieszka Miedlar, Challenges for Eigenvalue Computations in Breakthrough Applications (Zoom)

2-3pm: Lin Lin, Quantum algorithms for eigenvalue problems

3-4pm: break

4-5pm: Daniel Kressner, Randomized joint diagonalization

5-6pm: Jorge Garza Vargas, Global Convergence of the Hessenberg Shifted QR Algorithm