Is there a command in GAMS to solve multi objective problem.
How can I solve multi objective model by GAMS?
Thanks
See This
SOLVING MULTI-OBJECTIVE MODELS WITH GAMS
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I came across this Keras tutorial which is for a classification problem, but I'm trying to apply it to a regression one. Does anyone know why they change the dimension of the features? Has anybody used this for regression at all?
Also any link to a related tutorial would be appreciated.
I am reading the following paper about forecasting interest rates: https://onlinelibrary.wiley.com/doi/full/10.1002/for.2783.
In section 3.2.3 - the Hull-White model, it mentions that the parameters can be found by solving the optimization problem to minimize the difference between actual and model interest rates:
optimisation problem
The model interest rate is given by the solution of the Hull-White equation which involves a stochastic integral as shown here:
interest rate solution
Is there a well known method to deal with this kind of problem? Thank you!
I tried this : https://github.com/titu1994/tfdiffeq,
but had an issue and cannot proceed further - https://github.com/titu1994/tfdiffeq/issues/10
Tensorflow Probability has differentiable ODE solvers here.
You will get used to TFP solvers soon because the interface is much similar to tfdiffeq.
(But it also has some issues and I'm having trouble too😥)
I am learning the theory and the implementation of variational autoencoder by reading this.
In the documentation, it said optimize the following function: log{p(x|z)} + log{p(z)} - log{q(z|x)}. However, in the code, I am not able to understand why the implementation used cross-entropy to calculate log{p(x|z)}. Can someone please explain to me how cross-entropy is linked to log{p(x|z)}?
Thanks in advance.
I have a question regarding to programming simple quadratic assignment problem with Gurobi? As you know the objective function is not linear. Can we model and solve it with Gurobi (I am using Gurobi/python interface)