Line search failed even after resetting Hessian

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    • December 14, 2017 at 12:52 am #2814
      Anonymous

        Hello,

        I keep getting following messages (the run doesnt fail). Why is this happening? Havent really found an explanation here. Can anyone help?

        Thank you

        L.

         

        core.optimization.LineMinimizer: (3) Inaccurate G! step= 7.45058e-09 Deriv= -12.3859 Finite Diff= 1.0822e+09

        core.optimization.LineMinimizer: (3) Inaccurate G! step= 6.06331e-09 Deriv= -385.071 Finite Diff= 1.32981e+09

        core.optimization.LineMinimizer: (3) Inaccurate G! step= 3.88794e-07 Deriv= -385.071 Finite Diff= 2.07382e+07

        core.optimization.Minimizer: (3) Line search failed even after resetting Hessian; aborting at iter#57

      • December 25, 2017 at 4:54 pm #13973
        Anonymous

          Can anyone help please? I have looked for the answer everywhere

        • January 5, 2018 at 1:08 am #13976
          Anonymous

            Did you get any help?

            I’m also having this problem running AbinitioRelax with distance restraints. This thread https://www.rosettacommons.org/node/3388 indicates the error might be due to the input file data.

            In my case, I’m assuming the distance restraints are causing the problem. However, I’m getting the error after several rounds of minimization, so I’m not quite sure why it does not fail right away upon reading my cst file? I have 2 types of distance restraints:

            # experimental

            AtomPair O 198 N 187 BOUNDED 1.9 3.00 1.0 NOE

            etc..

            # other

            AtomPair CB 38 CB 111 GAUSSIANFUNC 5.0 3.0 RAPTX WEIGHT 0.786491

            etc…

            there is no format error thrown at any point by Rosetta, so I’m guessing the problem is elsewhere?

          • January 8, 2018 at 6:08 pm #13981
            Anonymous

              Hi, no I got no help.

              I saw the thread but I am not sure what is going on.

            • January 16, 2018 at 2:48 pm #13997
              Anonymous

                Is the anybody who can help me with that? The modelling seem to work but I am not sure if I can trust my decoys.

                I am really not sure where else to turn.

                 

              • January 19, 2018 at 11:47 pm #14003
                Anonymous

                  So I got help from the Baker Lab folks:

                  You may want to use the option:

                  -optimization::default_max_cycles 200

                  to save computing time.  According to Hahnbeom, 200 cycles is good enough rather than the default 2000.  He says you will likely run into the inaccurate G warning less often also.  Regardless, those warnings can be ignored.

                  • February 15, 2018 at 9:54 pm #14045
                    Anonymous

                      Thank you very much for your help!

                       

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