lossn_info.h

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/*
 * Copyright (c) 2012-2026, QORE Lab, Imperial College London
 * All rights reserved.
 */
#ifndef LINE_SOLVERS_LOSSN_INFO_H
#define LINE_SOLVERS_LOSSN_INFO_H
 
/**
 * @file
 * @ingroup line_solvers
 * The inputs of a loss network solved by SolverNC, kept beside the result.
 *
 * The reference stores them in `self.result.LossN` (struct nu, A, C, links,
 * method, options) at the end of `solver_nc_lossn_analyzer.m`, so that
 * `getAvgLinkTable` can report the per-link measures with the same method
 * without rebuilding the admission constraints. Each row of A n <= C is a
 * link, named in row order
 *
 *   GlobalMaxJobs        the region's global job cap,
 *   GlobalMaxMemory      its memory budget weighted by the class sizes,
 *   ClassMaxJobs:<class> a per-class job cap,
 *   Constraint<k>        the k-th linear constraint of setConstraint (1-based).
 *
 * EMPTY (valid == false) on every model that is not a loss network. The
 * values are held at double, the precision the tables report.
 */
 
#include <cstddef>
#include <cstdint>
#include <string>
#include <vector>
 
#include "line/util/matrix.h"
 
namespace line {
namespace solvers {
 
/** `self.result.LossN`. */
struct LossnInfo {
    bool valid = false;               ///< false outside a loss network
    std::vector<double> nu;           ///< (K) offered load per class
    Matrix<double> A;                 ///< (J x K) rows of the admission rule
    std::vector<double> C;            ///< (J) capacities
    std::vector<std::string> links;   ///< (J) link names, in row order
    std::string method;               ///< chosen method: exact, rec, erlangfp, asy or mci
    std::size_t samples = 0;          ///< mci sample count of the run (0 = not mci)
    std::uint64_t seed = 0;           ///< mci seed of the run
    bool empty() const { return !valid; }
};
 
}  // namespace solvers
}  // namespace line
 
#endif  // LINE_SOLVERS_LOSSN_INFO_H