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where $f : \mathbb{R}^n \to \mathbb{R}$ is continuously differentiable and $h : \mathbb{R}^n \to \mathbb{R} \cup \{\infty\}$ is lower semi-continuous and proper.
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Both $f$ and $h$ may be nonconvex.
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where $f: \mathbb{R}^n \to \mathbb{R}$ and $c: \mathbb{R}^n \to \mathbb{R}^m$ are continuously differentiable, and $h: \mathbb{R}^n \to \mathbb{R} \cup \{+\infty\}$ is lower semi-continuous.
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The nonsmooth objective $h$ can be a *regularizer* such as a sparsity-inducing penalty, model simple constraints such as $x$ belonging to a simple convex set, or be a combination of both.
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All $f$, $h$ and $c$ can be nonconvex.
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All solvers implemented in this package are JuliaSmoothOptimizers-compliant.
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They take a [`RegularizedNLPModel`](https://jso.dev/RegularizedProblems.jl/dev/reference#RegularizedProblems.RegularizedNLPModel) as input and return a [`GenericExecutionStats`](https://jso.dev/SolverCore.jl/stable/reference/#SolverCore.GenericExecutionStats).
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