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Add docs (#12)
* Add docs * format * fix names
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‎docs/Project.toml‎

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[deps]
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Documenter = "e30172f5-a6a5-5a46-863b-614d45cd2de4"
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ModelAnalyzer = "d1179b25-476b-425c-b826-c7787f0fff83"

‎docs/make.jl‎

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using Documenter, ModelAnalyzer
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makedocs(; sitename = "ModelAnalyzer.jl documentation")
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deploydocs(;
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repo = "github.com/jump-dev/ModelAnalyzer.jl.git",
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push_preview = true,
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)

‎docs/src/analyzer.md‎

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# ModelAnalyzer main API
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All the analysis modules in `ModelAnalyzer` follow the same main API.
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The main function to perform an analysis is:
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```@docs
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ModelAnalyzer.analyze
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```
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Once the analysis is performed, the resulting data structure can be summarized
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using:
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```@docs
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ModelAnalyzer.summarize
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```
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Alternatively, you can also query the types of issues found in the analysis
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and summarize them individually. The following functions are useful for this:
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```@docs
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ModelAnalyzer.list_of_issue_types
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ModelAnalyzer.list_of_issues
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```

‎docs/src/feasibility.md‎

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# Feasibility Analysis
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This module provides functionality to perform feasibility analysis on a JuMP model.
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This module follows the main API and is activated by the struct:
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```@docs
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ModelAnalyzer.Feasibility.Analyzer
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```
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The analysis will return issues of the abstract type:
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```@docs
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ModelAnalyzer.Feasibility.AbstractFeasibilityIssue
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```
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Specifically, the possible issues are:
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```@docs
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ModelAnalyzer.Feasibility.PrimalViolation
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ModelAnalyzer.Feasibility.DualViolation
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ModelAnalyzer.Feasibility.ComplemetarityViolation
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ModelAnalyzer.Feasibility.DualObjectiveMismatch
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ModelAnalyzer.Feasibility.PrimalObjectiveMismatch
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ModelAnalyzer.Feasibility.PrimalDualMismatch
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ModelAnalyzer.Feasibility.PrimalDualSolverMismatch
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```
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These issues are saved in the data structure that is returned from the
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`ModelAnalyzer.analyze` function:
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```@docs
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ModelAnalyzer.Feasibility.Data
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```

‎docs/src/index.md‎

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```@meta
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CurrentModule = ModelAnalyzer
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DocTestSetup = quote
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using ModelAnalyzer
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end
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```
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# ModelAnalyzer.jl
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This package provides tools for analyzing (and debugging) [JuMP](https://github.com/jump-dev/JuMP.jl) models.
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Three main functionalities are provided:
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1. **Numerical Analysis**: Check for numerical issues in the model, such as large and small coefficients,
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empty constraints, non-convex quadratic functions.
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2. **Feasibility Analysis**: Given an optimized model, or a candidate solution, check if the solutions is
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feasible and optimal (when possible). This includes checking the feasibility of the primal model and
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also the dual model (if available). Complementary slackness conditions are also checked (if applicable).
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3. **Infeasibility Analysis**: Given an unsolved of solved model, three steps are made to check for
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infeasibility:
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- Check bounds, integers and binaries consistency is also checked at this point.
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- Propagate bounds in constraints individually, to check if each constraint is infeasible
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given the current variable bounds. This is only done if bounds are ok.
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- Run an IIS (Irreducible Inconsistent Subsystem / irreducible infeasible sets)
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resolver algorithm to find a minimal infeasible subset of constraints.
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This is only done if no issues are found in the previous two steps.
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## Installation
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You can install the package using the Julia package manager. In the Julia REPL, run:
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```julia
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using Pkg
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Pkg.add("https://github.com/jump-dev/ModelAnalyzer.jl")
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```
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## Usage
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### Basic usage
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Here is a simple example of how to use the package:
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```julia
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using JuMP
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using ModelAnalyzer
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using HiGHS # or any other supported solver
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# Create a simple JuMP model
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model = Model(HiGHS.Optimizer)
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@variable(model, x >= 0)
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@variable(model, y >= 0)
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@constraint(model, c1, 2x + 3y == 5)
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@constraint(model, c2, x + 2y <= 3)
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@objective(model, Min, x + y)
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# Optimize the model
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optimize!(model)
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# either
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# Perform a numerical analysis of the model
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data = ModelAnalyzer.analyze(ModelAnalyzer.Numerical.Analyzer(), model)
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# print report
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ModelAnalyzer.summarize(data)
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# or
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# Check for solution feasibility and optimality
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data = ModelAnalyzer.analyze(ModelAnalyzer.Feasibility.Analyzer(), model)
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# print report
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ModelAnalyzer.summarize(data)
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# or
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# Infeasibility analysis (if the model was infeasible)
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data = ModelAnalyzer.analyze(
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ModelAnalyzer.Infeasibility.Analyzer(),
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model,
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optimizer = HiGHS.Optimizer,
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)
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# print report
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ModelAnalyzer.summarize(data)
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```
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The `ModelAnalyzer.analyze(...)` function can always take the keyword arguments:
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* `verbose = false` to condense the print output.
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* `max_issues = n` to limit the maximum number of issues to report for each type.
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For certain analysis modes, the `summarize` function can take additional arguments.
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### Advanced usage
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After any `ModelAnalyzer.analyze(...)` call is performed, the resulting data structure
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can be summarized using `ModelAnalyzer.summarize(data)` as show above,
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or it can be further inspected programmatically.
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```julia
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# given a `data` object obtained from `ModelAnalyzer.analyze(...)`
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# query the types os issues found in the analysis
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list = ModelAnalyzer.list_of_issue_types(data)
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# information about the types of issues found can be printed out
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ModelAnalyzer.summarize(data, list[1])
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# for each issue type, you can get the actual issues found in the analysis
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issues = ModelAnalyzer.list_of_issues(data, list[1])
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# the list of issues of the given type can be summarized with:
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ModelAnalyzer.summarize(data, issues)
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# infdividual issues can also be summarized
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ModelAnalyzer.summarize(data, issues[1])
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```

‎docs/src/infeasibility.md‎

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# Infeasibility Analysis
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This module provides functionality to perform infeasibility analysis on a JuMP model.
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This module follows the main API and is activated by the struct:
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```@docs
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ModelAnalyzer.Infeasibility.Analyzer
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```
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The analysis will return issues of the abstract type:
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```@docs
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ModelAnalyzer.Infeasibility.AbstractInfeasibilitylIssue
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```
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Specifically, the possible issues are:
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```@docs
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ModelAnalyzer.Infeasibility.InfeasibleBounds
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ModelAnalyzer.Infeasibility.InfeasibleIntegrality
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ModelAnalyzer.Infeasibility.InfeasibleConstraintRange
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ModelAnalyzer.Infeasibility.IrreducibleInfeasibleSubset
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```
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These issues are saved in the data structure that is returned from the
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`ModelAnalyzer.analyze` function:
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```@docs
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ModelAnalyzer.Infeasibility.Data
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```

‎docs/src/numerical.md‎

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# Numerical Analysis
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This module provides functionality to perform numerical analysis on a JuMP model.
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This module follows the main API and is activated by the struct:
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```@docs
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ModelAnalyzer.Numerical.Analyzer
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```
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The analysis will return issues of the abstract type:
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```@docs
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ModelAnalyzer.Numerical.AbstractNumericalIssue
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```
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Specifically the possible issues are:
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```@docs
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ModelAnalyzer.Numerical.VariableNotInConstraints
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ModelAnalyzer.Numerical.EmptyConstraint
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ModelAnalyzer.Numerical.VariableBoundAsConstraint
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ModelAnalyzer.Numerical.DenseConstraint
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ModelAnalyzer.Numerical.SmallMatrixCoefficient
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ModelAnalyzer.Numerical.LargeMatrixCoefficient
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ModelAnalyzer.Numerical.SmallBoundCoefficient
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ModelAnalyzer.Numerical.LargeBoundCoefficient
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ModelAnalyzer.Numerical.SmallRHSCoefficient
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ModelAnalyzer.Numerical.LargeRHSCoefficient
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ModelAnalyzer.Numerical.SmallObjectiveCoefficient
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ModelAnalyzer.Numerical.LargeObjectiveCoefficient
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ModelAnalyzer.Numerical.SmallObjectiveQuadraticCoefficient
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ModelAnalyzer.Numerical.LargeObjectiveQuadraticCoefficient
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ModelAnalyzer.Numerical.NonconvexQuadraticConstraint
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ModelAnalyzer.Numerical.SmallMatrixQuadraticCoefficient
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ModelAnalyzer.Numerical.LargeMatrixQuadraticCoefficient
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```
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These issues are saved in the data structure that is returned from the `ModelAnalyzer.analyze` function:
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```@docs
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ModelAnalyzer.Numerical.Data
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```

‎src/ModelAnalyzer.jl‎

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abstract type AbstractAnalyzer end
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"""
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analyze(analyzer::AbstractAnalyzer, model::JuMP.Model; kwargs...)
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Analyze a JuMP model using the specified analyzer.
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Depending on the analyzer, this keyword arguments might vary.
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This function will return an instance of `AbstractData` which contains the
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results of the analysis that can be further summarized or queried for issues.
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See [`summarize`](@ref), [`list_of_issues`](@ref), and
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[`list_of_issue_types`](@ref).
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"""
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function analyze end
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"""
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summarize([io::IO,] AbstractData; verbose = true, max_issues = typemax(Int), kwargs...)
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Print a summary of the analysis results contained in `AbstractData` to the
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specified IO stream. If no IO stream is provided, it defaults to `stdout`.
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The `verbose` flag controls whether to print detailed information about each
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issue (if `true`) or a concise summary (if `false`). The `max_issues` argument
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controls the maximum number of issues to display in the summary. If there are
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more issues than `max_issues`, only the first `max_issues` will be displayed.
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summarize([io::IO,] ::Type{T}; verbose = true) where {T<:AbstractIssue}
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This variant allows summarizing information of a specific type `T` (which must
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be a subtype of `AbstractIssue`). In the verbose case it will provide a text
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explaning the issue. In the non-verbose case it will provide just the issue
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name.
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summarize([io::IO,] issue::AbstractIssue; verbose = true)
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This variant allows summarizing a single issue instance of type `AbstractIssue`.
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"""
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function summarize end
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"""
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list_of_issue_types(data::AbstractData)
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Return a vector of `DataType` containing the types of issues found in the
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analysis results contained in `data`.
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"""
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function list_of_issue_types end
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"""
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list_of_issues(data::AbstractData, issue_type::Type{T}) where {T<:AbstractIssue}
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Return a vector of instances of `T` (which must be a subtype of `AbstractIssue`)
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found in the analysis results contained in `data`. This allows you to retrieve
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all instances of a specific issue type from the analysis results.
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"""
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function list_of_issues end
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function summarize(io::IO, ::Type{T}; verbose = true) where {T<:AbstractIssue}
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if verbose
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return _verbose_summarize(io, T)
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return summarize(stdout, data; kwargs...)
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end
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function analyze end
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function list_of_issues end
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function list_of_issue_types end
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function _verbose_summarize end
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function _summarize end
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