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|`Ball2`| Euclidean ball: `Ball2(None, r)` creates a Euclidean ball of radius `r` centered at the origin, and `Ball2(xc, r)` is a ball centered at point `xc` (list/np.array) |
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|`BallInf`| Ball of infinity norm:`BallInf(None, r)` creates an infinity-norm ball of radius `r` centered at the origin, and `BallInf(xc, r)` is an infinity ball centered at point `xc` (list/np.array) |
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|`Ball1`| L1 ball: `Ball(None, r)` creates an ell1-ball of radius `r` centered at the origin, and `BallInf(xc, r)` is an ell1-ball centered at point `xc` (list/np.array)|
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|`Sphere2`| Euclidean sphere: `Sphere2(None, r)` creates a Euclidean sphere of radius `r` centered at the origin, and `Sphere2(xc, r)` is a sphere centered at point `xc` (list/np.array) |
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|`Simplex`| A simplex of <em>size</em> $\alpha$ is a set of the form $\Delta_\alpha = \\{x \in \mathbb{R}^n {}:{} x_i \geq 0, \sum_i x_i = \alpha\\}$. Create one with `Simplex(alpha)`. Projections are computed using Condat's [fast projection method](https://link.springer.com/article/10.1007/s10107-015-0946-6). |
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|`Halfspace`| A halfspace is a set of the form $\\{u \in \mathbb{R}^{n_u} {}:{} \langle c, u\rangle \leq b \\}$, for a vector $c$ and a scalar $b$. The syntax is straightforwarrd: `Halfspace(c, b)`. |
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|`FiniteSet`| Finite set, $\\{u^{(1)},\ldots,u^{(m)}\\}$; the set of point is provided as a list of lists, for example, `FiniteSet([[1,2],[2,3],[4,5]])`. The commonly used set of binary numbers, $\\{0, 1\\}$, is created with `FiniteSet([[0], [1]])`. |
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|`NoConstraints`| No constraints - the whole $\mathbb{R}^{n}$|
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|`Rectangle`| Rectangle, $$R = \\{u \in \mathbb{R}^{n_u} {}:{} f_{\min} \leq u \leq f_{\max}\\},$$ for example, `Rectangle(fmin, fmax)`|
|`EpigraphSquaredNorm`| The epigraph of the squared Eucliden norm is a set of the form $X = \\{(z, t) \in \mathbb{R}^{n+1}: \Vert z \Vert \leq t\\}$. |
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|`CartesianProduct`| Cartesian product of any of the above. See more information below. |
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|`AffineSpace`| An affine space is a set of the form $\\{x\in\mathbb{R}^n {}:{} Ax = b\\}$ for a matrix $A\in \mathbb{R}^p$ and vector $b$. Docs: ([Rust](https://docs.rs/optimization_engine/latest/optimization_engine/constraints/struct.AffineSpace.html), [Python](https://alphaville.github.io/optimization-engine/api-dox/html/opengen.constraints.html#module-opengen.constraints.affine_space)) |
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|`Ball2`| Euclidean ball: `Ball2(None, r)` creates a Euclidean ball of radius `r` centered at the origin, and `Ball2(xc, r)` is a ball centered at point `xc` (list/np.array) Docs: ([Rust](https://docs.rs/optimization_engine/latest/optimization_engine/constraints/struct.Ball2.html), [Python](https://alphaville.github.io/optimization-engine/api-dox/html/opengen.constraints.html#module-opengen.constraints.ball2)) |
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|`BallInf`| Ball of infinity norm:`BallInf(None, r)` creates an infinity-norm ball of radius `r` centered at the origin, and `BallInf(xc, r)` is an infinity ball centered at point `xc` (list/np.array) Docs: ([Rust](https://docs.rs/optimization_engine/latest/optimization_engine/constraints/struct.BallInf.html), [Python](https://alphaville.github.io/optimization-engine/api-dox/html/opengen.constraints.html#module-opengen.constraints.ball_inf)) |
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|`Ball1`| L1 ball: `Ball(None, r)` creates an ell1-ball of radius `r` centered at the origin, and `BallInf(xc, r)` is an ell1-ball centered at point `xc` (list/np.array) Docs: ([Rust](https://docs.rs/optimization_engine/latest/optimization_engine/constraints/struct.Ball1.html), [Python](https://alphaville.github.io/optimization-engine/api-dox/html/opengen.constraints.html#module-opengen.constraints.ball1)) |
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|`Sphere2`| Euclidean sphere: `Sphere2(None, r)` creates a Euclidean sphere of radius `r` centered at the origin, and `Sphere2(xc, r)` is a sphere centered at point `xc` (list/np.array) Docs: ([Rust](https://docs.rs/optimization_engine/latest/optimization_engine/constraints/struct.Sphere2.html), [Python](https://alphaville.github.io/optimization-engine/api-dox/html/opengen.constraints.html#module-opengen.constraints.sphere2)) |
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|`Simplex`| A simplex of <em>size</em> $\alpha$ is a set of the form $\Delta_\alpha = \\{x \in \mathbb{R}^n {}:{} x_i \geq 0, \sum_i x_i = \alpha\\}$. Create one with `Simplex(alpha)`. Projections are computed using Condat's [fast projection method](https://link.springer.com/article/10.1007/s10107-015-0946-6). Docs: ([Rust](https://docs.rs/optimization_engine/latest/optimization_engine/constraints/struct.Simplex.html), [Python](https://alphaville.github.io/optimization-engine/api-dox/html/opengen.constraints.html#module-opengen.constraints.simplex)) |
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|`Halfspace`| A halfspace is a set of the form $\\{u \in \mathbb{R}^{n_u} {}:{} \langle c, u\rangle \leq b \\}$, for a vector $c$ and a scalar $b$. The syntax is straightforward: `Halfspace(c, b)`. Docs: ([Rust](https://docs.rs/optimization_engine/latest/optimization_engine/constraints/struct.Halfspace.html), [Python](https://alphaville.github.io/optimization-engine/api-dox/html/opengen.constraints.html#module-opengen.constraints.halfspace)) |
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|`Hyperplane`| A hyperplane is a set given by $H=\\{x \in \mathbb{R}^n {}:{} c^\intercal x =b \\}$. Docs: [Rust](https://docs.rs/optimization_engine/latest/optimization_engine/constraints/struct.Hyperplane.html)|
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|`FiniteSet`| Finite set, $\\{u^{(1)},\ldots,u^{(m)}\\}$; the set of point is provided as a list of lists, for example, `FiniteSet([[1,2],[2,3],[4,5]])`. The commonly used set of binary numbers, $\\{0, 1\\}$, is created with `FiniteSet([[0], [1]])`. Docs: ([Rust](https://docs.rs/optimization_engine/latest/optimization_engine/constraints/struct.FiniteSet.html), [Python](https://alphaville.github.io/optimization-engine/api-dox/html/opengen.constraints.html#module-opengen.constraints.finite_set)) |
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|`NoConstraints`| No constraints - the whole $\mathbb{R}^{n}$ Docs: ([Rust](https://docs.rs/optimization_engine/latest/optimization_engine/constraints/struct.NoConstraints.html), [Python](https://alphaville.github.io/optimization-engine/api-dox/html/opengen.constraints.html#module-opengen.constraints.no_constraints)) |
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|`Rectangle`| Rectangle, $$R = \\{u \in \mathbb{R}^{n_u} {}:{} f_{\min} \leq u \leq f_{\max}\\},$$ for example, `Rectangle(fmin, fmax)` Docs: ([Rust](https://docs.rs/optimization_engine/latest/optimization_engine/constraints/struct.Rectangle.html), [Python](https://alphaville.github.io/optimization-engine/api-dox/html/opengen.constraints.html#module-opengen.constraints.rectangle)) |
|`EpigraphSquaredNorm`| The epigraph of the squared Euclidean norm is a set of the form $X = \\{(z, t) \in \mathbb{R}^{n+1}: \Vert z \Vert \leq t\\}$. Docs: ([Rust](https://docs.rs/optimization_engine/latest/optimization_engine/constraints/struct.EpigraphSquaredNorm.html), Python: to be implemented) |
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|`CartesianProduct`| Cartesian product of any of the above. See more information below. Docs: ([Rust](https://docs.rs/optimization_engine/latest/optimization_engine/constraints/struct.CartesianProduct.html), [Python](https://alphaville.github.io/optimization-engine/api-dox/html/opengen.constraints.html#module-opengen.constraints.cartesian)) |
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|`Zero`| The set $\\{0\\}$. Docs: ([Rust](https://docs.rs/optimization_engine/latest/optimization_engine/constraints/struct.Zero.html), [Python](https://alphaville.github.io/optimization-engine/api-dox/html/opengen.constraints.html#module-opengen.constraints.zero)) |
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@@ -424,7 +427,7 @@ Note here that the solver performed 6 outer
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