We lifted 6 functions out of this paper's own repositories and ran 4 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| mvono/PyGauss | canonical | 4 of 6 |
| Function | Status | Where it lives |
|---|---|---|
| col_vector_norms | Ran | mvono/PyGauss/pygauss/utils.py code served (permissive licence) · get_code("368718f126c7a20c") |
| diagonal_form | Ran | mvono/PyGauss/pygauss/utils.py code served (permissive licence) · get_code("381a944879a3b4bd") |
| sampler_band | Ran | mvono/PyGauss/pygauss/direct_sampling.py code served (permissive licence) · get_code("2f9f15d43cbbaf03") |
| sampler_factorization | Ran | mvono/PyGauss/pygauss/direct_sampling.py code served (permissive licence) · get_code("bc624a1a1ecb2db7") |
| CG | Not yet run | mvono/PyGauss/pygauss/utils.py code served (permissive licence) · get_code("568b3f2a3ea03bd8") |
| sampler_circulant | Not yet run | mvono/PyGauss/pygauss/direct_sampling.py code served (permissive licence) · get_code("4e0fcc70b3b86bf8") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Efficient sampling from a high-dimensional Gaussian distribution is an old but high-stake issue. Vanilla Cholesky samplers imply a computational cost and memory requirements which can rapidly become prohibitive in high dimension. To tackle these issues, multiple methods have been proposed from different communities ranging from iterative numerical linear algebra to Markov chain Monte Carlo (MCMC) approaches. Surprisingly, no complete review and comparison of these methods have been conducted. This paper aims at reviewing all these approaches by pointing out their differences, close relations, benefits and limitations. In addition to this state of the art, this paper proposes a unifying Gaussian simulation framework by deriving a stochastic counterpart of the celebrated proximal point algorithm in optimization. This framework offers a novel and unifying revisit of most of the existing MCMC approaches while extending them. Guidelines to choose the appropriate Gaussian simulation method for a given sampling problem in high dimension are proposed and illustrated with numerical examples.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2010.01510")
get_code_for_paper("2010.01510")
have("2010.01510")
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