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Abhijit Brahme (Work Log) (view source)
Revision as of 13:05, 14 November 2017
, 13:05, 14 November 2017no edit summary
<onlyinclude>[[Abhijit Brahme]] [[Work Logs]][[Abhijit Brahme (Work Log) | (log page)]]</onlyinclude>
It seems that nonlinearcons_msm.m outputs the appropriate constraints. <br>
Need to look at how to implement this in Gurobi for Matlab.
==7/18/2017==
Helped Diana learn how to implement the circle plotter for startup locations. <br>
Read up more about the genetic algorithm function in matlab. <br>
==7/19/2017==
Created a separate script for gmm_2stage_estimation which is used to implement gurobi. <br>
Was able to implement the lower bound,upper bound of the variables using gurobi. <br>
Am finding it difficult to describe the objective function in terms of the variables themselves. <br>
If I could do that, this would be a whole lot easier. <br>
The objective function does not match the readme.pdf the author has provided. <br>
Tried running the same program with the altered objective function. <br>
==7/20/2017==
Currently timing each of the functions involved with this script. <br>
Matlab encountered an error which forced the closing of the script. <br>
Will re-run
==7/21/2017==
Ran the monte and timed the functions. <br>
[[File:monteruntime.png]]
==7/24/2017==
Since the code actually runs now, thought about ways to add more info to what we know about the code. <br>
Still don't know how the function fed into the genetic algorithm actually relates to the documentation. <br>
Some ideas for tomorrow:
1. use the actual generations and values from the original script and time it
==7/25/2017==
Did as suggested above. <br>
Here are the results. <br>
[[File:runtime2.png]] <br>
When comparing the two, it seems like changing the generation size just makes the function take longer to run, but even after one generation the fitness function does not improve its value. <br>
I don't understand why you'd need more than one generation.
==7/28/2017==
Fixed the monte with James' help and utilized integer linear programming inside the genetic algorithm. <br>
Previously, the author was using a linear program and rounding the results (not advisable). <br>
We figure there must be some error in the monte because the genetic algorithm does not update. <br>
==7/31/2017==
The value passed into the genetic algorithm is either 0 or .1642. <br>
Here are the values of the key variables that determine the input of the genetic algorithm:
1. mm: either .1111 or .2462
2. M0: always .1111
3. W: either 1 or .0240
4. L: always 9
James seems to think the "trueval" is already set close enough to the optimal. <br>
This seems correct. <br>
==8/1/2017==
I changed trueval to be a larger margin to see if the genetic algorithm changes. <br>
And it didn't do anything. The function value always remains at 0 and the variables retain their values. <br>
We changed the market size, etc and now the function seems to change its values. <br>
==8/2/2017==
Got the function to work and produce a different outcome. <br>
Seems to be a problem with the strip_mkt_resample. <br>
Got a lot of resulting "NaN" values, traced it to stderr. <br>
We changed a value to an outer product in stderr and ran the code. <br>
==8/3/2017==
Here is the output when incorporating the outer product instead. <br>
[[File:monte_using_outer_product.png]]
==8/4/2017==
All the master files will run appropriately with the proper "task". <br>
These files are located here:
1. E:\McNair\Projects\MatchingEntrepsToVC\AdjustedCode
You literally open up the master.m file and press run.
[[Category: Work Log]]
