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mlosch |
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#!/bin/bash |
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# these are some basic instructions to an optimization of |
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# MITgcm/verification/tutorial_global_oce_optim with |
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# MITgcm_offline/mlosch/optim_m1qn3 |
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# Some tweaking is definitely possible and not described here. |
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# |
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# |
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cd MITgcm/verification |
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# this is just to compile and run the model for testing. |
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# I use TAF (just because I have it and OpenAD is a pain to compile on a Mac), |
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# but that should not make any difference |
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./testreport -t tutorial_global_oce_optim -adm -j 4 -ncad |
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# this is the result: |
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# G D M C A F |
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# e p a R o d D |
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# n n k u s G G |
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# 2 d e n t r r |
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# Y Y Y Y 15>16< 7 pass tutorial_global_oce_optim (e=0, w=2) |
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# here is the cost function value that I get |
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# (PID.TID 0000.0001) local fc = 0.620023228182336D+01 |
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# (PID.TID 0000.0001) global fc = 0.620023228182336D+01 |
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# now I download and compile optim_m1qn3 |
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cd ../../ |
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cvs co MITgcm_contrib/mlosch/optim_m1qn3 |
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cd MITgcm_contrib/mlosch/optim_m1qn3 |
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# edit Makefile to adjust to your platform. For me this involves choosing |
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# the correct CPP command and setting SUFF=for (because MacOS is |
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# case-insensitive in my case) |
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make depend |
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make |
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# then I get optim.x |
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cd ../../../MITgcm/verification/tutorial_global_oce_optim/run |
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cp ../../../../MITgcm_contrib/mlosch/optim_m1qn3/optim.x . |
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# - turn off the gradient check (in data.pkg: useGrdchk = .FALSE.) |
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# - tweak the namelist files data.ctrl and data.optim to the compiler needs |
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# (I need a "/" to terminate a namelist) |
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# - I would replace fmin with dfminFrac = 0.1 (expected reduction of 10%) |
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# in data.optim&OPTIM to be independent of the absolute value of the |
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# cost function. When you run ./mitgcmuv_ad with this you need to comment |
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# out dfminFrac, because mitgcmuv_ad does not know about this namelist |
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# parameter (something to be fixed) |
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# - set numiter=100, nfunc=10, or some other large value. nfunc*numiter is |
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# the number of simulations that are allowed in total. This number should be |
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# much larger than numiter, because you may need more than one function call |
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# (= run of mitgcmuv_ad) per iteration, see m1qn3 docs for details |
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# - add an empty namelist &M1QN3 to data.optim |
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# &M1QN3 |
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# / |
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./optim.x > opt0.txt |
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# I get a lot of output in opt0.txt, which is easier to read with |
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# less -S opt0.out (to truncate long lines) |
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# essentially I have the same value for the cost function |
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# ================================================== |
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# Large Scale Optimization with off-line capability. |
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# ================================================== |
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# OPTIM_READPARMS: Control options have been read. |
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# OPTIM_READPARMS: Minimization options have been read. |
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# OPTIM_READDATA: Reading cost function and gradient of cost function |
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# for optimization cycle: 0 |
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# OPTIM_READDATA: opened file ecco_cost_MIT_CE_000.opt0000 |
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# OPTIM_READDATA: nvartype 1 |
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# OPTIM_READDATA: nvarlength 2315 |
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# OPTIM_READDATA: yctrlid MIT_CE_000 |
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# OPTIM_READDATA: filenopt 0 |
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# OPTIM_READDATA: fileff 6.2002322818233591 |
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# OPTIM_READDATA: fileiG 1 |
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# OPTIM_READDATA: filejG 1 |
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# OPTIM_READDATA: filensx 2 |
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# OPTIM_READDATA: filensy 2 |
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# [...] |
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# OPTIM_READDATA: end of optim_readdata |
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# OPTIM_READPARMS: Iteration number = 0 |
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# number of control variables = 2315 |
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# cost function value in ecco_ctrl = 6.2002322818233591 |
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# expected cost function minimum = 5.5802090536410232 |
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# expected cost function decrease = 0.62002322818233591 |
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# Data will be read from the following file: ecco_ctrl_MIT_CE_000.opt0000 |
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# OPTIM_SUB: Calling m1qn3_optim for iteration: 0 |
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# OPTIM_SUB: with nn, REAL_BYTE = 2315 4 |
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# OPTIM_SUB: read model state |
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# OPTIM_READDATA: Reading control vector |
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# for optimization cycle: 0 |
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# OPTIM_READDATA: opened file ecco_ctrl_MIT_CE_000.opt0000 |
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# OPTIM_READDATA: nvartype 1 |
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# OPTIM_READDATA: nvarlength 2315 |
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# OPTIM_READDATA: yctrlid MIT_CE_000 |
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# OPTIM_READDATA: filenopt 0 |
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# OPTIM_READDATA: fileff 6.2002322818233591 |
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# OPTIM_READDATA: fileiG 1 |
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# OPTIM_READDATA: filejG 1 |
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# OPTIM_READDATA: filensx 2 |
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# OPTIM_READDATA: filensy 2 |
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# OPTIM_READDATA: end of optim_readdata |
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# OPTIM_READDATA: Reading cost function and gradient of cost function |
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# for optimization cycle: 0 |
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# OPTIM_READDATA: opened file ecco_cost_MIT_CE_000.opt0000 |
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# OPTIM_READDATA: nvartype 1 |
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# OPTIM_READDATA: nvarlength 2315 |
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# OPTIM_READDATA: yctrlid MIT_CE_000 |
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# OPTIM_READDATA: filenopt 0 |
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# OPTIM_READDATA: fileff 6.2002322818233591 |
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# OPTIM_READDATA: fileiG 1 |
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# OPTIM_READDATA: filejG 1 |
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# OPTIM_READDATA: filensx 2 |
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# OPTIM_READDATA: filensy 2 |
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# OPTIM_READDATA: end of optim_readdata |
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# OPTIM_SUB after reading ecco_ctrl and ecco_cost: |
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# OPTIM_SUB nn = 2315 |
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# OPTIM_SUB objf = 6.2002322818233591 |
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# OPTIM_SUB xx(1) = 0.0000000000000000 |
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# OPTIM_SUB adxx(1) = -6.7879882408306003E-005 |
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# OPTIM_SUB: cold start, optimcycle = 0 |
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# OPTIM_SUB: call m1qn3_offline ........ |
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# OPTIM_SUB: ........................... |
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# OPTIM_SUB: returned from m1qn3_offline |
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# OPTIM_SUB: nn = 2315 |
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# OPTIM_SUB: xx(1) = 0.51934864251896229 0.73000729032300782 |
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# OPTIM_SUB: adxx(1) = -6.7879882408306003E-005 -9.5413379312958568E-005 |
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# OPTIM_SUB: omode = -1 |
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# OPTIM_SUB: niter = 1 |
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# OPTIM_SUB: nsim = 10000 |
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# OPTIM_SUB: reverse = 1 |
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# OPTIM_SUB: mean(xx) = 0.16365068483545642 |
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# OPTIM_SUB: max(xx) = 4.6525355815045790 |
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# OPTIM_SUB: min(xx) = -9.3326211896764324 |
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# OPTIM_SUB: std(xx) = 15.613450260548481 |
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# OPTIM_STORE_M1QN3: saving the state of m1qn3 in OPWARM.opt0001 |
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# OPTIM_SUB: writing 2315 sized control to file ecco_ctrl |
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# OPTIM_WRITEDATA: Writing new control vector to file(s) |
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# for optimization cycle: 1 |
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# OPTIM_WRITEDATA: nvartype 1 |
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# OPTIM_WRITEDATA: nvarlength 2315 |
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# OPTIM_WRITEDATA: yctrlid MIT_CE_000 |
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# OPTIM_WRITEDATA: nopt 1 |
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# OPTIM_WRITEDATA: ff -9999.0000000000000 |
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# OPTIM_WRITEDATA: iG 1 |
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# OPTIM_WRITEDATA: jG 1 |
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# OPTIM_WRITEDATA: nsx 2 |
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# OPTIM_WRITEDATA: nsy 2 |
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# OPTIM_WRITEDATA: end of optim_writedata, icvoffset 2315 |
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# ====================================== |
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# Large Scale Optimization run finished. |
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# ====================================== |
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# ff = -9999 is an intentional dummy value. |
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# now you can organize the rest in a loop. In bash, it could look like this: |
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# first comment out dfminFrac, because mitgcmuv_ad does not know about this, |
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# dfminFrac is really only needed in the zeroth iteration |
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# |
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# tabula rasa |
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rm ecco_c* OPWARM.* m1qn3_output.txt |
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myiter=0 |
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cat > data.optim <<EOF # |
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# ******************************** |
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# Off-line optimization parameters |
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# ******************************** |
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&OPTIM |
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optimcycle=${myiter}, |
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numiter=100, |
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nfunc=100, |
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#dfminFrac = 0.1, |
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iprint=10, |
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nupdate=8, |
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/ |
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&M1QN3 |
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/ |
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EOF |
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while (( $myiter < 20 )) |
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do |
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# formatter iteration count |
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it=`echo $myiter | awk '{printf "%03i",$1}'` |
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echo "iteration ${myiter}" |
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# comment out dfminFrac from data.optim |
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sed -i '' 's/.*dfminFrac.*/#dfminFrac = 0.1,/' data.optim |
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# increment counter in data.optim |
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sed -i .${it} "s/.*optimcycle.*/ optimcycle=${myiter},/" data.optim |
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./mitgcmuv_ad > output${it}.txt |
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# add dfminFrac = 0.1, |
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sed -i '' 's/#dfminFrac.*/ dfminFrac = 0.1,/' data.optim |
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./optim.x > opt${it}.txt |
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# increase counter for next iteration |
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((myiter++)) |
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done |
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# grep "fc " output001.txt |
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# (PID.TID 0000.0001) early fc = 0.000000000000000D+00 |
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# (PID.TID 0000.0001) local fc = 0.132325873958879D+02 |
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# (PID.TID 0000.0001) global fc = 0.132325873958879D+02 |
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# grep "global fc " output???.txt |
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# output000.txt:(PID.TID 0000.0001) global fc = 0.620023228182336D+01 |
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# output001.txt:(PID.TID 0000.0001) global fc = 0.132325873958879D+02 |
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# output002.txt:(PID.TID 0000.0001) global fc = 0.615567811433600D+01 |
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# output003.txt:(PID.TID 0000.0001) global fc = 0.615556878869932D+01 |
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# output004.txt:(PID.TID 0000.0001) global fc = 0.615547009131471D+01 |
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# as you can see, the improvement is not very good after the initial steps. |
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# and the optimization will not be successful in the end, i.e. satisfy |
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# the tolerance set by epsg. |
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# This is likely because the number of timesteps in data is small (20) |
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# for this test. Try longer intergations (e.g. 1 year) and wait longer |
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# check out m1qn3_output.txt, which records the output of m1qn3 |
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# the python script plotfc.py greps the cost function values out of |
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# m1qn3_ouput.txt and plots them and the number of simulations per iteration |
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# which typically increase with decreasing cost function |
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