Approximate computing techniques within numerical linear algebra algorithms are raising major interests in the context of exascale-era supercomputers and problems. By approximating all or certain strategic parts of the computation, approximate computing methods can substantially reduce the time, memory, and energy consumption of scientific computing algorithms. In this context, Julia has offered a fantastic playground for the development of approximate computing techniques, such as mixed precision algorithms, randomization and sketching, or low-rank approximations. This minisymposium features talks from researchers and practitioners describing their use of Julia for studying the numerical behavior or leveraging the computational benefits of approximate computing techniques.
JuliaCon Global 2026 will host a minisymposium on "Approximate Computing in Numerical Linear Algebra" by Jonas Schulze, Nicolas Venkovic, Andreas Varga and Mantas Mikaitis find out more on pretalx.com/juliacon-202... and submit your talks at juliacon.org/2026/cfp
#julialang #numerics
