Comments (3)
I described my workflow here: https://www.youtube.com/watch?v=QVmU29rCjaA
I have since moved from Atom to VS Code, but other than that most of the workflow is the same.
from optimization.jl.
It just needs someone to do it.
from optimization.jl.
I'm interested in working on this as I need constraints and FiniteDiff.jl
for my work.
I've never worked on a separate repo like this before, though - @ChrisRackauckas do you have any resources that I can refer to for help with branching from a separate repo -> making changes -> submitting the pull request (on Windows)? I tried it myself with remotes in VS Code but things kept breaking with the Julia language server somehow. If you have those that'd be much appreciated and I'd try and implement this.
from optimization.jl.
Related Issues (20)
- `MethodError: objects of type Nothing are not callable` when `lb` and `ub` are used with `NelderMead` HOT 9
- PolyesterForwardDiff not loading HOT 3
- No documentation for latest release HOT 1
- PRIMA lib errors with `AutoForwardDiff` HOT 3
- Support LBFGSB.jl HOT 12
- Include `searchdirection` in `OptimizationState` HOT 2
- The `callback` appears to be called for linesearch iterations HOT 1
- `PolyOpt` only accept functions without any extra inputs HOT 1
- US spelling preferred? HOT 1
- Augmented Lagrangian HOT 5
- Multithreading support for Optimizers like BBO
- Is there currently a feasible way to use NamedTuple or ComponentArray as x0 for Optimization.jl HOT 1
- How to get latest minimizer (u) instead of the best one for optimization? HOT 5
- Callback signature doesn't match with the docs for PRIMA HOT 1
- Optimization.LBFGS() can not compute the gradient HOT 4
- BBO always returns retcode Failure HOT 1
- ERROR: UndefVarError: `NoAD` not defined HOT 5
- callback-generated stopping criteria no longer work in OptimizationNLopt
- BFGS fails with user-supplied derivative and bounds (regression in v3.25.0, v3.25.1)
- OptimizationOptimJL.Optim.BFGS() missing tests for user-supplied derivatives (grad), upper/lower bounds, and the combination
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from optimization.jl.