Monte carlo simulation optimisation framework C#

[Daniel Duffy]

L-BFGS and CG
Unconstrained optimization. Function gradient is needed.

Levenberg-Marquardt algorithm
Unconstrained or boundary constrained optimization.

Bound and linear equality/inequality constrained optimization
Linearly constrained optimization.

Constrained quadratic programming with box/linear constraints

ALL theese methods require gradints and looks like they are not for me
Or i am wrong&How i can write gradient?

You can help me if just say Which method i shoud choose? maybe you know one or two good?

If you want derivative-free models, what about Differential Evolution?

 

[John TRD]

If you want derivative-free models, what about Differential Evolution?

Yes i will look on it
Looks like i have misunderstandin of Monte Carlo
What id diffrences between runs and tests in each run in Monte carlo?

 

[John TRD]

I?

Daniel how this Monte carlo model called?Maybe sequential monte carlo method
Set a number of Runs and a number of Tests per Run. During each optimization pass, the optimizer tries a different set of random parameters for each run, remembering the values that maximize the chosen metric as it continues.

Runs
Controls the number of times that the MC optimizer shrinks the parameter windows to re-center them around the resultant closest-to-optimal value.

Test per Run
Controls the number of random tests within each Pass without shrinking or moving the windows.

Assume that you have chosen "Profit" to be maximized. At the start of each pass, the range of random values is decreased and centered on the most profitable value determined in the previous pass. The Monte Carlo Optimizer keeps shrinking the parameter windows as it proceeds, until it winds up centered on a set of values that maximize the targeted metric.

"prevent the sampling distribution from shrinking too quickly" If we choose Runs 100 and Test 1 we will find just local maximum/And Range will be shinked and san all samples will be near this local maximum
Test-is random with wide range