opl | Syntax meant for easy creation | Interpreter library

Out of curiosity, I am trying to figure out why the PLS regression coefficients obtained with pls differ from the coefficients obtained with plsRglm, ropls, or plsdepot which all provide the same results.

Here is some code to start with. I have tried to play with the scale, center, and method arguments of the plsr function... but no success so far.

How to write those encircled constraints

in CPLEX OPL?

I have a column with a number of rows. I want to row-combine the string based on a given multiple.

Dears,

Currently, I am using OPL-DoCplex to solve a problem using CP. The basic model was built in OPL then I called it into python by DoCplex. I have the following questions: 1- In the OPL model, there are decision expressions (dexpr) that were not import to python. How can I do that or where can I find the imported dexpr if any. 2- How can I stop the solver after finding each feasible solution do some post-processing then run the solver again. Is that possible?

Thanks in advance Mohamed

The solution of my variable returns a negative value, while I defined it as a dvar flaot+, how is this possible? Here is my code:

I am new to IBM's CMPLEX Optimization Studio and I try to understand what is the best approach to take for realizing my goal. I am trying to implement a decision optimization solution, expressed as a Mix Integer Linear Program by creating a relevant representation for my problem and then interface it with the CMPLEX solver.

Initially, I started developing the model in the OPL language. However, as I intend to be dynamically calling the solver with the same model but with different data, from my C++ application, I realized that it makes more sense to build the model and call the solver through the CMPLEX C++ API, part of the Concert Technology (#reff1, #reff2). Of course, the interfaces the Concert Technology exposes for building your model are much more low level, in terms of how easy it is to express it, compared to building your model in OPL and running it directly. Then, I learn about the "OPL Interfaces" which also exposes a C++ API from which if understand it correctly, you can load your model from an OLP definition and use custom data sources, and invoke the solver.

So my question is how the OPL Interfaces and CMPLEX APIs (Concert Technology) for a specific language compare if both can be used for the same purpose, to build and execute the optimization model? I understand that the OPL Interfaces and the C++ API of OPL are based on the C++ Concert Technology. If the goal is to quickly test the model but with custom data input dynamically given by my C++ application, should I go for the OPL INterfaces, or there a reason why going for the CMPLEX C++ API in Concert Technology is better?

Software Version: IBM ILOG CMPLEX Optimization Studio 20.1.0

I have multiple CSV files with 4 common name patterns. I am using purrr::map to import all 4 groups as a 4 different dataframes. I have 10 csv files per name pattern. name patterns: DLL HH comp_XXX, SHH DD comp_XXX, OPL FG comp_XXX and SLL FG comp_XXX

I used this snippet 4 times to cycle through the files and bind them all together and get 4 dfs , DLL, SHH, OPL and SLL dataframe at the end ( separate call for each name)

Here an example for DLL HH comp *** name pattern

Dears,

I have an optimization model that was built using OPL. The model works fine but I would like to use an objective function that is defined in Python. My question is can I use doopl to call the OPL model into python and use the objective function from Python (not from OPL).

Thanks

I have an Overpass query like this: