{"id":24213687,"url":"https://github.com/sarodyatawatta/calibration-pytorch-test","last_synced_at":"2026-04-12T22:34:37.633Z","repository":{"id":167816652,"uuid":"228680175","full_name":"SarodYatawatta/calibration-pytorch-test","owner":"SarodYatawatta","description":"Radio interferometric calibration with PyTorch. 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Further details are given [in this paper](https://ieeexplore.ieee.org/document/8755567) and [also this](https://arxiv.org/abs/2003.00986). Also see [this introduction](http://sagecal.sourceforge.net/pytorch/index.html).\n\nFiles included are:\n\n``` lbfgsnew.py ```: New LBFGS optimizer\n\n``` lbfgsb.py ```: Bound constrained LBFGS optimizer\n\n``` run_calibration.py ```: Run a simple calibration, like\n\n``` \nrun_calibration.py --solver_name=LBFGSB \n```\n\nAvailable options for ```--solver_name``` are ```LBFGS, LBFGSB, SGD, ADAM```.\n\n\n\u003cimg src=\"time.png\" alt=\"reduction of calibration cost\" width=\"700\"/\u003e\n\nHere is an image showing the reduction of calibration error (Student's T loss) with minibatch (CPU time) for LBFGS and Adam. Adam runs faster but slower to converge. LBFGS uses 1 epoch and Adam uses 4 epochs in the image. The minibatch size is 1/10-th of the full dataset.\n\nFor a much faster, C/CUDA version of the LBFGS optimizer, follow [this link](https://github.com/nlesc-dirac/sagecal/tree/master/test/Dirac).\n\nFor a completely different method to calibrate, see also [ManOpt](https://github.com/NicolasBoumal/manopt/blob/master/examples/radio_interferometric_calibration.m).\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsarodyatawatta%2Fcalibration-pytorch-test","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fsarodyatawatta%2Fcalibration-pytorch-test","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsarodyatawatta%2Fcalibration-pytorch-test/lists"}