Comments (4)
This could this be an instance of the more general case g(x) = f(Ax), for convex, proximable f, I guess.
from proximaloperators.jl.
Even better, although you wouldn't be able to use a QP solver for the prox of f(Ax), only dual PANOC (NAMA). But if it is a QP, then we should be able to have the option to use something like GUROBI for small problems.
from proximaloperators.jl.
PR #54 contains an implementation of this (no warm-start yet).
from proximaloperators.jl.
I just submitted a paper for a QP solver. It is supposed to be really efficent projecting on Ax=b, Cx<=d, when the set of constraints is relatively small, by solving the dual problem.
The algorithm, completely in julia is found here: https://github.com/mfalt/QPDAS.jl
We could add it as an option for "solver".
The interface is almost the same, we would have to rewrite when lower bound is equal to upper bound to an equality, and there is no added efficiency for using xmin/xmax instead of lb/ub.
And it allows for any of these bounds to be changed without re-factorizing, and warmstart is done automatically.
It should beat OSQP pretty comfortably unless the number of inequalites is large.
from proximaloperators.jl.
Related Issues (20)
- Add prox! method for IndPSD handling matrices
- possible test failure in upcoming Julia version 1.5 HOT 1
- Projection on Stiefel manifold HOT 1
- Polishing docstrings
- Computing proximal operator of a constrained convex function HOT 5
- Nonnegative least squares example HOT 3
- TagBot trigger issue HOT 18
- TV norm prox HOT 6
- Replace exponential cone projection? HOT 3
- Lightweight “base” package with core definitions
- Sums of norms HOT 2
- SqrNormL2 expects λ to be positive HOT 3
- Is ProximableFunction no longer supported? HOT 2
- calculus function fields HOT 4
- nonconvex prox calculus rule HOT 1
- Multi-threaded operators evaluation
- Improve pretty printing HOT 3
- GPU support via CuArrays.jl HOT 12
- Prox of Sum HOT 2
- Locally Low-Rank operator HOT 2
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from proximaloperators.jl.