Solution class

The Solution class stores the result of solving a single Problem object in multiscape.

A Solution object contains the original problem definition, the core optimization output returned by the solver, user-facing summary tables, diagnostics about the solve process, and metadata describing how the solution was obtained.

Objects of this class are typically created by solve.

Value

No return value. This page documents the Solution class.

Details

Conceptual role

The Solution class represents the output of one optimization run.

It should be understood as the single-run counterpart of the modelling workflow:


Problem -> solve() -> Solution

Thus, a Solution object does not replace the original Problem; instead, it keeps a reference to it in the problem field and augments it with optimization results.

Single-run semantics

A Solution corresponds to one realized solution of one configured optimization problem. This may come from:

a single-objective solve,
one run of a weighted multi-objective workflow,
one \(\epsilon\)-constraint subproblem,
one AUGMECON subproblem,
or any other workflow that ultimately produces one optimizer output.

When multiple runs are generated, they are typically collected in a separate SolutionSet object rather than a single Solution.

Internal structure

A Solution object separates results into several layers:

problem: The original Problem object used to generate the solution.
solution: A named list containing the core optimization output. This may include the objective value, raw model variable vector, decoded decision vectors, and evaluated objective values by alias.
summary: A named list of user-facing summary tables, typically derived from the original problem and the solved decisions. These tables are intended for inspection, plotting, reporting, and downstream analysis.
diagnostics: A named list containing solver diagnostics such as status, runtime, optimality gap, solver name, and runtime settings.
method: A named list describing the optimization method used to obtain the solution.
meta: A named list containing additional metadata.
name: A character(1) identifier for the solution object.

Core optimization output

The solution field stores the solver-facing result. Typical entries may include:

objective: The scalar objective value returned by the solver for this run.
vector: The raw internal solution vector in model-variable order.
alias_values: Objective values evaluated for registered aliases, when available.

The raw solution vector may contain not only user-facing decision variables such as planning-unit or action decisions, but also auxiliary variables introduced internally by the model builder.

User-facing summaries

The summary field stores derived tables intended for interpretation rather than solver interaction. Typical entries include:

pu: Planning-unit summary table.
actions: Planning unit–action allocation summary table.
features: Feature-level outcome summary table.
targets: Target-achievement summary table, when targets were part of the problem.

These tables are the main source used by user-facing accessors such as get_pu, get_actions, get_features, and get_targets.

Diagnostics

The diagnostics field stores metadata about the optimization process, including solver status and runtime information. Typical entries may include:

solver name,
runtime in seconds,
optimality gap,
number of cores,
time limit,
status code or status label.

These values describe how the solution was obtained, not the content of the solution itself.

Printing

The print() method is intended as a concise diagnostic summary. It reports:

solver status,
objective value,
optimality gap,
runtime,
counts of selected planning units and actions,
target fulfillment summary when available,
evaluated objective alias values when available,
and basic solver information.

This summary is intended for quick inspection. More detailed exploration should use the stored summary, solution, and diagnostics fields directly, or the dedicated accessor functions.

Fields

problem: The Problem object used to generate the solution.
solution: A named list containing the core optimization result.
summary: A named list of user-facing summary tables.
diagnostics: A named list of solver diagnostics.
method: A named list describing the method used.
meta: A named list of additional metadata.
name: A character(1) name for the solution object.

Methods

print(): Print a concise summary of the solution, including status, objective value, runtime, selection counts, and target fulfillment.
show(): Alias of print().
repr(): Return a short one-line representation of the solution.

Value

Details

Fields

Methods

See also