mosek 11.1.1

Rust API for MOSEK optimization tools
Documentation 
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//!
//!  Copyright : Copyright (c) MOSEK ApS, Denmark. All rights reserved.
//!
//!  File : solutionquality.rs
//!
//!  Purpose :   To demonstrate how to examine the quality of a solution.

extern crate mosek;
extern crate itertools;

use mosek::{Task,Streamtype,Solsta,Soltype};
use std::env;
use itertools::Either::{self,*};

fn main() -> Result<(),String> {
    let mut args = env::args();
    if args.len() < 2 {
        println!("Syntax: solutionquality FILENAME");
        return Err("Invalid argument list".to_string())
    }
    let _ = args.next();
    let filename = args.next().unwrap();
    solutionquality(Right(filename))
}

fn solutionquality(filename : Either<String,String>) -> Result<(),String> {
    let mut task = Task::new().unwrap().with_callbacks();
    task.put_stream_callback(Streamtype::LOG, |msg| print!("{}",msg))?;
    // We assume that a problem file was given as the first command
    // line argument (received in `args')
    match filename {
        Right(filename) => task.read_data (filename.as_str())?,
        Left(data) => task.read_ptf_string(data.as_str())?
    }

    // Solve the problem
    let _ = task.optimize()?;

    task.solution_summary(Streamtype::LOG)?;

    let solsta = task.get_sol_sta(Soltype::BAS)?;

    let mut pobj        : f64 = 0.0;
    let mut pviolcon    : f64 = 0.0;
    let mut pviolvar    : f64 = 0.0;
    let mut pviolbarvar : f64 = 0.0;
    let mut pviolcones  : f64 = 0.0;
    let mut pviolitg    : f64 = 0.0;
    let mut dobj        : f64 = 0.0;
    let mut dviolcon    : f64 = 0.0;
    let mut dviolvar    : f64 = 0.0;
    let mut dviolbarvar : f64 = 0.0;
    let mut dviolcones  : f64 = 0.0;

    task.get_solution_info(Soltype::BAS,
                           & mut pobj, & mut pviolcon, & mut pviolvar, & mut pviolbarvar, & mut pviolcones, & mut pviolitg,
                           & mut dobj, & mut dviolcon, & mut dviolvar, & mut dviolbarvar, & mut dviolcones)?;
    match solsta {
        Solsta::OPTIMAL => {
            let abs_obj_gap = (dobj-pobj).abs();
            let rel_obj_gap = abs_obj_gap / (1.0 + f64::min(pobj.abs(), dobj.abs()));
            let max_primal_viol = f64::max(pviolcon, pviolvar);
            let max_primal_viol = f64::max(max_primal_viol  , pviolbarvar);
            let max_primal_viol = f64::max(max_primal_viol  , pviolcones);

            let max_dual_viol   = f64::max(dviolcon, dviolvar);
            let max_dual_viol   = f64::max(max_dual_viol, dviolbarvar);
            let max_dual_viol   = f64::max(max_dual_viol, dviolcones);

            // Assume the application needs the solution to be within
            //    1e-6 ofoptimality in an absolute sense. Another approach
            //   would be looking at the relative objective gap

            println!("Customized solution information.");
            println!("  Absolute objective gap: {:.3e}", abs_obj_gap);
            println!("  Relative objective gap: {:.3e}", rel_obj_gap);
            println!("  Max primal violation  : {:.3e}", max_primal_viol);
            println!("  Max dual violation    : {:.3e}", max_dual_viol);

            let mut accepted = true;

            if rel_obj_gap > 1e-6 {
                println!("Warning: The relative objective gap is LARGE.");
                accepted = false;
            }

            // We will accept a primal infeasibility of 1e-8 and
            // dual infeasibility of 1e-6. These number should chosen problem
            // dependent.
            if max_primal_viol > 1e-8 {
                println!("Warning: Primal violation is too LARGE");
                accepted = false;
            }

            if max_dual_viol > 1e-6 {
                println!("Warning: Dual violation is too LARGE.");
                accepted = false;
            }

            if accepted {
                let numvar = task.get_num_var()?;
                println!("Optimal primal solution");
                let mut xx = vec![0.0; numvar as usize];
                task.get_xx(Soltype::BAS,xx.as_mut_slice())?;
                for (j,&xj) in (0..numvar).zip(xx.iter()) {
                    println!("x[{}]: {}",j,xj);
                }
            } else {
                // print etailed information about the solution
                task.analyze_solution(Streamtype::LOG, Soltype::BAS)?;
            }
        },
        Solsta::DUAL_INFEAS_CER => println!("Primal or dual infeasibility certificate found."),
        Solsta::UNKNOWN => println!("The status of the solution is unknown."),
        _ => println!("Other solution status"),
    }
    Ok(())
}


#[cfg(test)]
mod tests {

    const DFLT_FILE : &str = "Task
Objective
    Maximize + 3 @x0 + @x1 + 5 @x2 + @x3
Constraints
    @c0 [30] + 3 @x0 + @x1 + 2 @x2
    @c1 [15;+inf] + 2 @x0 + @x1 + 3 @x2 + @x3
    @c2 [-inf;25] + 2 @x1 + 3 @x3
Variables
    @x0 [0;+inf]
    @x1 [0;10]
    @x2 [0;+inf]
    @x3 [0;+inf]
";

    #[test]
    fn test() {
        super::solutionquality(itertools::Either::Left(DFLT_FILE.to_string())).unwrap();
    }
}