{"push_id":977345309,"size":1,"distinct_size":1,"ref":"refs/heads/master","head":"59a10f157325752c9c9759c290d7dc7580c27e9a","before":"2f974db51164fe27551f0c078feb1a03616d265a","commits":[{"sha":"59a10f157325752c9c9759c290d7dc7580c27e9a","author":{"email":"d40cedb4b0bb6630f73a8d00f14972dd3b19c562@gmail.com","name":"Ying ZHANG"},"message":"Create README.md\n\nThis package tries to solve the matrix low-rank approximation problem by alternating direction methods of multipliers. This problem can find its position in many applications. Specifically, we try to recover a feasible solution for the optimal power flow problem when the SDP (semidefinite programming) relaxation is inexact.","distinct":true,"url":"https://api.github.com/repos/yingcuhk/RLRA-OPF/commits/59a10f157325752c9c9759c290d7dc7580c27e9a"}]}