Carleton Coffrin
Carleton Coffrin
You might want to implement the ability to read FlatZinc models. This would open up a large library of MiniZinc models for testing.
This point is related to #101 #126. Future versions of Alpine can leverage the https://github.com/JuliaOpt/MINLPTests.jl repo for testing core JuMP features and edge cases (e.g. feasibility problems). I would also...
Consider adding two new example that use: - [ ] `@NL` macros with advanced nonlinear functions (e.g. trig, powers, high order polynomials) - [ ] MI-Conic model using a continuous...
See if adding a [simple presolver](http://www.optimization-online.org/DB_HTML/2017/10/6275.html) will improve performance.
Implement some of form of LDS. A natural first choice would be to use the `Pseudo Cost` values for defined the branching order.
It might be more valuable to parrallize multiple starts of the NLP solver, rather than the tree search. Worth exploring.
In cases where converging to local infeasible points results in not finding any feasible solution, allow the solver to continue branching on infeasible nodes, in hopes of recovering feasibility after...
Following @Vaibhavdixit02 example [here](https://discourse.julialang.org/t/ac-optimal-power-flow-in-various-nonlinear-optimization-frameworks/78486/31?u=ccoffrin), tried changing, ``` import ForwardDiff optprob = Optimization.OptimizationFunction(opf_objective, Optimization.AutoForwardDiff(); cons=opf_constraints) ``` to ``` import ModelingToolkit optprob = Optimization.OptimizationFunction(opf_objective, Optimization.AutoModelingToolkit(true,true); cons=opf_constraints) ``` I got the following error....
This example does not appear to work in Julia v1.0. The function `A_mul_Bc!` is undefined. https://github.com/JuliaDiff/ReverseDiff.jl/blob/master/examples/jacobian.jl#L9
Based on discussion with @mlubin and @blegat, the best longterm strategy is to implement all formulations in PowerModels using MOI's SOC and RSOC constraints. Bridges then can be used to...