9 articles in this category
Every spectral solver I've touched in anger was tuned on a matrix that never showed up in production. The lab data was clean, square, and tame. Then c...
You've got a glitch. A nanosecond pulse, a radar chirp, a switching transient. You set your oscilloscope to the widest bandwidth possible, thinking 'm...
You've built a spectral sparse recovery pipeline. It works beautifully on synthetic Gaussian noise. Then you deploy it on real data—and the estimates ...
Spectral clustering sits at the intersection of graph theory and linear algebra. It works beautifully on non-convex blobs, image segmentation, and com...
If you've ever tried to shrink a graph for faster computation, you know spectral coarsening is a double-edged sword. It can cut compute time by orders...
Spectral optimization sounds neat on paper: shift eigenvalues, smooth signals, boost cluster separability. But pick a graph that's a scale-free mess o...
You have smooth data — infinitely differentiable, even. The kind of function spectral methods were built for. Yet your Chebyshev expansion, that suppo...
Imagine you are solving a substantial eigenvalue issue for a bridge block. The solver converges— then a load shift by 0.1%. Your eigenpairs jump, and ...
If you have ever tried to compute a spectrum from unevenly spaced data—say, stock prices logged at random times, or astronomical observations interrup...