Dataset for A Blocked Linear Method for Optimizing Large Parameter Sets in Variational Monte Carlo

Luning Zhao & Eric Neuscamman
We present a modification to variational Monte Carlo’s linear method optimization scheme that addresses a critical memory bottleneck while maintaining compatibility with both the traditional ground state variational principle and our recently-introduced variational principle for excited states. For wave function ansatzes with tens of thou- sands of variables, our modification reduces the required memory per parallel process from tens of gigabytes to hundreds of megabytes, making the methodology a much bet- ter fit for modern...
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