# Getting Started

This section describes the process of creating a Clarabel.rs model, populating its settings and problem data, solving the problem and obtaining and understanding results. It is assumed here that you are building your project using `cargo`

.

The first step is to make the Clarabel solver a dependency in your project by adding:

```
[dependencies]
clarabel = {version = "0"}
```

to your project's `Cargo.toml`

file. Then bring the solver into scope in your source files:

```
use clarabel::algebra::*;
use clarabel::solver::*;
```

The `algebra`

module defines the `CscMatrix`

type for defining matrices in compressed sparse column format. It also contains some basic utilities for creating and manipulating sparse matrices.

## Problem Format

Clarabel solves optimisation problems in the format:

\[\begin{array}{ll} \text{minimize} & \textstyle{\frac{1}{2}}x^\top Px + q^\top x\\ \text{subject to} & Ax + s = b \\ & s \in \mathcal{K}, \end{array}\]

with decision variables $x \in \mathbb{R}^n$, $s \in \mathbb{R}^m$ and data matrices $P=P^\top \succeq 0$, $q \in \mathbb{R}^n$, $A \in \mathbb{R}^{m \times n}$, and $b \in \mathbb{R}^m$. The convex cone $\mathcal{K}$ is a composition of smaller convex cones $\mathcal{K} = \mathcal{K}_1 \times \mathcal{K}_2 \dots \mathcal{K}_p$. Equality conditions can be modelled in this format using the solver's ZeroCone type.

To initialize the solver with an optimisation problem we require three things:

- The objective function, i.e. the matrix
`P`

and the vector`q`

in $\frac{1}{2}x^\top P x + q^\top x$. - The data matrix
`A`

and vector`b`

, along with a description of the composite cone $\mathcal{K}$ and the dimensions of its constituent pieces. - A
`settings`

object that specifies how Clarabel solves the problem.

## Objective Function

To set the objective function of your optimisation problem simply define the square positive semidefinite matrix $P \in \mathrm{R}^{n\times n}$ and the vector $q \in \mathrm{R}^{n}$.

Suppose that we have a problem with decision variable $x \in \mathbb{R}^3$ and our objective function is:

\[\begin{equation*} \min ~ \frac{1}{2} \left[ \begin{array}{l} x_1 \\ x_2 \\x_3 \end{array} \right] ^T \left[ \begin{array}{rrr} 3.0 & 1.0 & -1.0 \\ 1.0 & 4.0 & 2.0 \\ -1.0 & 2.0 & 5.0 \end{array} \right] \left[ \begin{array}{l} x_1 \\ x_2 \\x_3 \end{array} \right] + \left[ \begin{array}{r} 1 \\ 2 \\-3 \end{array} \right]^T \left[ \begin{array}{l} x_1 \\ x_2 \\x_3 \end{array} \right] \end{equation*}\]

Clarabel expects the `P`

matrix to be supplied in Compressed Sparse Column format. `P`

is assumed by the solver to be symmetric and only values in the upper triangular part of `P`

are needed by the solver, i.e. you only need to provide

\[\begin{equation*} P = \left[ \begin{array}{rrr} 3.0 & 1.0 & -1.0 \\ ⋅ & 4.0 & 2.0 \\ ⋅ & ⋅ & 5.0 \end{array} \right] \end{equation*}\]

The Clarabel default implementation in Rust expects matrix data as a CscMatrix object and provides a set of basic utilities for sparse matrix construction. We can define our cost data as

```
let P = CscMatrix::new(
3, // m
3, // n
vec![0, 1, 3, 6], // colptr
vec![0, 0, 1, 0, 1, 2], // rowval
vec![3., 1., 4., -1., 2., 5.], // nzval
);
let q = vec![1., 2., -3.];
```

To specify `P = I`

, you can use

`let P = CscMatrix::identity(2);`

where in this case we have had to be specific about the floating point data type we want. To use a zero matrix (e.g. if solving an LP), you can use

`let P = CscMatrix::spalloc((2,2),0);`

to construct a sparse matrix with no entries.

The solver will not conduct any check on the internal correctness of matrices passed in CscMatrix format. You can do this externally using the `check_format`

method, e.g.:

`assert!(P.check_format().is_ok());`

## Constraints

The Clarabel interface expects constraints to be presented in the single vectorized form $Ax + s = b, s \in \mathcal{K}$, where $\mathcal{K} = \mathcal{K}_1 \times \dots \times \mathcal{K}_p$ and each $\mathcal{K}_i$ is one of the solver's supported cone types.

Suppose that we have a problem with decision variable $x \in \mathbb{R}^3$ and our constraints are:

- A single equality constraint $x_1 + x_2 - x_3 = 1$.
- A pair of inequalities such that $x_2$ and $x_3$ are each less than 2.
- A second order cone constraint on the 3-dimensional vector $x$.

For the three constraints above, we have

\[ \begin{align*} A_{eq} &= \left[ \begin{array}{lll} 1 & 1 & -1 \end{array} \right], \quad & b_{eq} &= \left[ \begin{array}{l} 1 \end{array} \right], \\[4ex] A_{ineq} &= \left[ \begin{array}{lll} 0 & 1 & 0 \\ 0 & 0 & 1 \end{array} \right], \quad & b_{ineq} &= \left[ \begin{array}{l} 2\\2 \end{array} \right], \\[4ex] A_{soc} &= \left[ \begin{array}{rrr} -1 & 0 & 0 \\ 0 & -1 & 0 \\ 0 & 0 & -1 \end{array} \right], \quad & b_{soc} &= \left[ \begin{array}{l} 0 \\0 \\0 \end{array} \right] \end{align*} \]

We can define our constraint data as

```
let Aeq = CscMatrix::new(
1, // m
3, // n
vec![0, 1, 2, 3], // colptr
vec![0, 0, 0], // rowval
vec![1., 1., -1.], // nzval
);
let Aineq = CscMatrix::new(
2, // m
3, // n
vec![0, 0, 1, 2], // colptr
vec![0, 1], // rowval
vec![1., 1.], // nzval
);
let mut Asoc = CscMatrix::identity(3);
Asoc.negate();
let A = CscMatrix::vcat(&Aeq, &Aineq);
let A = CscMatrix::vcat(&A, &Asoc);
let b = vec![1., 2., 2., 0., 0., 0.];
// optional correctness check
assert!(A.check_format().is_ok());
```

Clarabel.rs expects to receive a vector of cone specifications. For the above constraints we should also define

```
# Clarabel.jl cone specification
let cones = [ZeroConeT(1), NonnegativeConeT(2), SecondOrderConeT(3)];
```

There is no restriction on the ordering of the cones that appear in `cones`

, nor on the number of instances of each type that appear. Your input vector `b`

should be compatible with the sum of the cone dimensions.

Note carefully the signs in the above example. The inequality condition is $A_{ineq} x \le b_{ineq}$, which is equivalent to $A_{ineq} x + s = b_{ineq}$ with $s \ge 0$, i.e. $s$ in the Nonnegative cone. The SOC condition is $x \in \mathcal{K}_{SOC}$, or equivalently $-x + s = 0$ with $s \in \mathcal{K}_{SOC}$.

## Solver Settings

Solver settings for the Clarabel's default implementation in Rust are stored in a `DefaultSettings`

object and can be modified by the user. To create a settings object using all defaults you can call the constructor directly:

`let settings = DefaultSettings::default();`

Alternatively, you can use the `DefaultSettingsBuilder`

to specify custom settings. For example, if you want to disable verbose printing and set a 5 second time limit on the solver, you can use:

```
let settings = DefaultSettingsBuilder::default()
.verbose(false)
.time_limit(1.)
.build()
.unwrap();
```

The full set of user configurable solver settings are listed in the Rust API Reference.

## Making a Solver

Finally populate the solver with problem data and solve:

```
let mut solver = DefaultSolver::new(&P, &q, &A, &b, &cones, settings);
solver.solve();
```

## Results

Once the solver algorithm terminates you can inspect the solution using the `solution`

field of the solver. The primal solution will be in `solution.x`

and the dual solution in `solution.z`

, e.g.

`println!("Solution = {:?}", solver.solution.x);`

The outcome of the solve is specified in `solver.solution.status`

and will be one of the following :

Status Code | Description |
---|---|

Unsolved | Default value, only occurs prior to calling `solve` |

Solved | Solution found |

PrimalInfeasible | Problem is primal infeasible |

DualInfeasible | Problem is dual infeasible |

MaxIterations | Solver halted after reaching iteration limit |

MaxTime | Solver halted after reaching time limit |

The total solution time is available in `solver.solution.solve_time`

.

Be careful to retrieve solver solutions from the `solution`

that is returned by the solver, or directly from a `solver`

object from the `solver.solution`

field. Do *not* use the `solver.variables`

, since these have both homogenization and equilibration scaling applied and therefore do *not* solve the optimization problem posed to the solver.