Struct SolveResult 

pub struct SolveResult { /* private fields */ }

Full outcome of a solve attempt.

Implementations

impl SolveResult

pub fn new( status: SolveStatus, solution: Option<Solution>, objective_value: Option<f64>, diagnostics: SolveDiagnostics, ) -> Self

pub fn status(&self) -> SolveStatus

Examples found in repository

examples/basic_l2.rs (line 26)

fn main() -> Result<(), Box<dyn std::error::Error>> {
    // Problem:
    // minimize_x 1/2 ||M x - r||_2^2
    //
    // with
    // M = [[1, 0],
    //      [0, 1],
    //      [1, 1]]
    // r = [1, 2, 2.5]
    //
    // Least-squares intuition:
    // x should be close to [0.8333, 1.8333]

    let residual = LinearResidual::new(
        Matrix::from_row_major(3, 2, vec![1.0, 0.0, 0.0, 1.0, 1.0, 1.0])?,
        vec![1.0, 2.0, 2.5],
    )?;

    let problem = ConstrainedResidualProblem::new(residual, Loss::L2Squared)?;

    let result = problem.solve()?;

    println!("=== basic_l2 ===");
    println!("status      : {:?}", result.status());
    println!("objective   : {:?}", result.objective_value());
    println!("diagnostics : {:?}", result.diagnostics());

    if let Some(solution) = result.solution() {
        println!("x           : {:?}", solution.x());
        println!("residual    : {:?}", solution.residual());
    } else {
        println!("no solution returned");
    }

    Ok(())
}

examples/clarabel_constrained_l2.rs (line 25)

fn main() -> Result<(), Box<dyn std::error::Error>> {
    let residual = LinearResidual::new(
        Matrix::from_row_major(3, 2, vec![1.0, 0.0, 0.0, 1.0, 1.0, 1.0])?,
        vec![1.0, 2.0, 2.5],
    )?;

    let eq = LinearEqualities::new(Matrix::from_row_major(1, 2, vec![1.0, -1.0])?, vec![0.0])?;

    let ineq = LinearInequalities::new(Matrix::from_row_major(1, 2, vec![1.0, 1.0])?, vec![3.0])?;

    let bounds = Bounds::new(vec![Some(0.0), Some(0.0)], vec![Some(10.0), Some(10.0)])?;

    let problem = ConstrainedResidualProblem::new(residual, Loss::L2Squared)?
        .add_equalities(eq)?
        .add_inequalities(ineq)?
        .with_bounds(bounds)?;

    let result = problem.solve()?;

    println!("status      : {:?}", result.status());
    println!("objective   : {:?}", result.objective_value());
    println!("diagnostics : {:?}", result.diagnostics());

    if let Some(solution) = result.solution() {
        println!("x           : {:?}", solution.x());
        println!("residual    : {:?}", solution.residual());
    }

    Ok(())
}

pub fn solution(&self) -> Option<&Solution>

Examples found in repository

examples/basic_l2.rs (line 30)

fn main() -> Result<(), Box<dyn std::error::Error>> {
    // Problem:
    // minimize_x 1/2 ||M x - r||_2^2
    //
    // with
    // M = [[1, 0],
    //      [0, 1],
    //      [1, 1]]
    // r = [1, 2, 2.5]
    //
    // Least-squares intuition:
    // x should be close to [0.8333, 1.8333]

    let residual = LinearResidual::new(
        Matrix::from_row_major(3, 2, vec![1.0, 0.0, 0.0, 1.0, 1.0, 1.0])?,
        vec![1.0, 2.0, 2.5],
    )?;

    let problem = ConstrainedResidualProblem::new(residual, Loss::L2Squared)?;

    let result = problem.solve()?;

    println!("=== basic_l2 ===");
    println!("status      : {:?}", result.status());
    println!("objective   : {:?}", result.objective_value());
    println!("diagnostics : {:?}", result.diagnostics());

    if let Some(solution) = result.solution() {
        println!("x           : {:?}", solution.x());
        println!("residual    : {:?}", solution.residual());
    } else {
        println!("no solution returned");
    }

    Ok(())
}

examples/clarabel_constrained_l2.rs (line 29)

fn main() -> Result<(), Box<dyn std::error::Error>> {
    let residual = LinearResidual::new(
        Matrix::from_row_major(3, 2, vec![1.0, 0.0, 0.0, 1.0, 1.0, 1.0])?,
        vec![1.0, 2.0, 2.5],
    )?;

    let eq = LinearEqualities::new(Matrix::from_row_major(1, 2, vec![1.0, -1.0])?, vec![0.0])?;

    let ineq = LinearInequalities::new(Matrix::from_row_major(1, 2, vec![1.0, 1.0])?, vec![3.0])?;

    let bounds = Bounds::new(vec![Some(0.0), Some(0.0)], vec![Some(10.0), Some(10.0)])?;

    let problem = ConstrainedResidualProblem::new(residual, Loss::L2Squared)?
        .add_equalities(eq)?
        .add_inequalities(ineq)?
        .with_bounds(bounds)?;

    let result = problem.solve()?;

    println!("status      : {:?}", result.status());
    println!("objective   : {:?}", result.objective_value());
    println!("diagnostics : {:?}", result.diagnostics());

    if let Some(solution) = result.solution() {
        println!("x           : {:?}", solution.x());
        println!("residual    : {:?}", solution.residual());
    }

    Ok(())
}

pub fn objective_value(&self) -> Option<f64>

Examples found in repository

examples/basic_l2.rs (line 27)

fn main() -> Result<(), Box<dyn std::error::Error>> {
    // Problem:
    // minimize_x 1/2 ||M x - r||_2^2
    //
    // with
    // M = [[1, 0],
    //      [0, 1],
    //      [1, 1]]
    // r = [1, 2, 2.5]
    //
    // Least-squares intuition:
    // x should be close to [0.8333, 1.8333]

    let residual = LinearResidual::new(
        Matrix::from_row_major(3, 2, vec![1.0, 0.0, 0.0, 1.0, 1.0, 1.0])?,
        vec![1.0, 2.0, 2.5],
    )?;

    let problem = ConstrainedResidualProblem::new(residual, Loss::L2Squared)?;

    let result = problem.solve()?;

    println!("=== basic_l2 ===");
    println!("status      : {:?}", result.status());
    println!("objective   : {:?}", result.objective_value());
    println!("diagnostics : {:?}", result.diagnostics());

    if let Some(solution) = result.solution() {
        println!("x           : {:?}", solution.x());
        println!("residual    : {:?}", solution.residual());
    } else {
        println!("no solution returned");
    }

    Ok(())
}

examples/clarabel_constrained_l2.rs (line 26)

fn main() -> Result<(), Box<dyn std::error::Error>> {
    let residual = LinearResidual::new(
        Matrix::from_row_major(3, 2, vec![1.0, 0.0, 0.0, 1.0, 1.0, 1.0])?,
        vec![1.0, 2.0, 2.5],
    )?;

    let eq = LinearEqualities::new(Matrix::from_row_major(1, 2, vec![1.0, -1.0])?, vec![0.0])?;

    let ineq = LinearInequalities::new(Matrix::from_row_major(1, 2, vec![1.0, 1.0])?, vec![3.0])?;

    let bounds = Bounds::new(vec![Some(0.0), Some(0.0)], vec![Some(10.0), Some(10.0)])?;

    let problem = ConstrainedResidualProblem::new(residual, Loss::L2Squared)?
        .add_equalities(eq)?
        .add_inequalities(ineq)?
        .with_bounds(bounds)?;

    let result = problem.solve()?;

    println!("status      : {:?}", result.status());
    println!("objective   : {:?}", result.objective_value());
    println!("diagnostics : {:?}", result.diagnostics());

    if let Some(solution) = result.solution() {
        println!("x           : {:?}", solution.x());
        println!("residual    : {:?}", solution.residual());
    }

    Ok(())
}

pub fn diagnostics(&self) -> &SolveDiagnostics

Examples found in repository

examples/basic_l2.rs (line 28)

fn main() -> Result<(), Box<dyn std::error::Error>> {
    // Problem:
    // minimize_x 1/2 ||M x - r||_2^2
    //
    // with
    // M = [[1, 0],
    //      [0, 1],
    //      [1, 1]]
    // r = [1, 2, 2.5]
    //
    // Least-squares intuition:
    // x should be close to [0.8333, 1.8333]

    let residual = LinearResidual::new(
        Matrix::from_row_major(3, 2, vec![1.0, 0.0, 0.0, 1.0, 1.0, 1.0])?,
        vec![1.0, 2.0, 2.5],
    )?;

    let problem = ConstrainedResidualProblem::new(residual, Loss::L2Squared)?;

    let result = problem.solve()?;

    println!("=== basic_l2 ===");
    println!("status      : {:?}", result.status());
    println!("objective   : {:?}", result.objective_value());
    println!("diagnostics : {:?}", result.diagnostics());

    if let Some(solution) = result.solution() {
        println!("x           : {:?}", solution.x());
        println!("residual    : {:?}", solution.residual());
    } else {
        println!("no solution returned");
    }

    Ok(())
}

examples/clarabel_constrained_l2.rs (line 27)

fn main() -> Result<(), Box<dyn std::error::Error>> {
    let residual = LinearResidual::new(
        Matrix::from_row_major(3, 2, vec![1.0, 0.0, 0.0, 1.0, 1.0, 1.0])?,
        vec![1.0, 2.0, 2.5],
    )?;

    let eq = LinearEqualities::new(Matrix::from_row_major(1, 2, vec![1.0, -1.0])?, vec![0.0])?;

    let ineq = LinearInequalities::new(Matrix::from_row_major(1, 2, vec![1.0, 1.0])?, vec![3.0])?;

    let bounds = Bounds::new(vec![Some(0.0), Some(0.0)], vec![Some(10.0), Some(10.0)])?;

    let problem = ConstrainedResidualProblem::new(residual, Loss::L2Squared)?
        .add_equalities(eq)?
        .add_inequalities(ineq)?
        .with_bounds(bounds)?;

    let result = problem.solve()?;

    println!("status      : {:?}", result.status());
    println!("objective   : {:?}", result.objective_value());
    println!("diagnostics : {:?}", result.diagnostics());

    if let Some(solution) = result.solution() {
        println!("x           : {:?}", solution.x());
        println!("residual    : {:?}", solution.residual());
    }

    Ok(())
}

pub fn is_solved(&self) -> bool

Trait Implementations

impl Clone for SolveResult

fn clone(&self) -> SolveResult

Returns a duplicate of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl Debug for SolveResult

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl PartialEq for SolveResult

fn eq(&self, other: &SolveResult) -> bool

Tests for self and other values to be equal, and is used by ==.

fn ne(&self, other: &Rhs) -> bool

Tests for !=. The default implementation is almost always sufficient, and should not be overridden without very good reason.

impl StructuralPartialEq for SolveResult