Fredrik Bagge Carlson
Fredrik Bagge Carlson
The following is a correct specification of composition, where `sys1` is provided to `systems=[sys1]` when creating `sys2`: ```julia @variables t D = Differential(t) @variables x(t)=0 eqs1 = [D(x) ~ -x]...
```julia using Symbolics: variables using Symbolics function cartpole(x, u) mc, mp, l, g = 1.0, 0.2, 0.5, 9.81 q = x[SA[1, 2]] qd = x[SA[3, 4]] s = sin(q[2]) c...
Consider the following result of calling `cse`, the expression `(sin)(ˍ₋arg1[2])` appears as the second found subexpression, but this subex also appears in the first and third expressions. SymPy does the...
The following snippet computes a Hessian in which each element is individually simplified (`map(simplify, hs.nzval)`. Some of the elements get simplified to zero, when evaluating the expression using numerical input,...
- [x] This is waiting on a new release of SciMLBase containing the `lag_h` field in `OptimizationFunction`. - [ ] Updating all the AD backends to provide the `lag_h` function....
[MadNLP.jl](https://github.com/MadNLP/MadNLP.jl) is a NLP solver that is very similar to Ipopt (by design) but implemented in pure julia. Performance is excellent and it supports - Second-order derivative information - Matrices...
The options for solving constrained problems are currently rather limited. It'd be nice if any constraints $l \leq c(x) \leq u$ present could be automatically transformed to indicator functions and...
When solving multiple related optimization problems, the overhead of calling `solve` may be important. Here, I'm benchmarking how much overhead is associated with calling `solve` by predefining the problem and...
If one tries to solve the same problem twice using the same `solver = Ipopt.Optimizer()`, a cryptical error appears ```julia using Optimization, OptimizationMOI, Ipopt rosenbrock(u,p) = (p[1] - u[1])^2 +...
Looks like there's something fishy in `nlopt_set_xtol_abs` ```julia using Optimization, Symbolics, OptimizationNLopt, SparseArrays using OptimizationNLopt.NLopt N = 10 x0 = randn(N) x = Symbolics.variables(:x, 1:N) p = Symbolics.variable(:p) cost =...