Improve docs

docs/src/index.md

@@ -7,29 +7,35 @@ end
 
 # ModelAnalyzer.jl
 
-This package provides tools for analyzing (and debugging) [JuMP](https://github.com/jump-dev/JuMP.jl) models.
+This package provides tools for analyzing (and debugging)
+[JuMP](https://github.com/jump-dev/JuMP.jl) models.
 
 Three main functionalities are provided:
 
-1. **Numerical Analysis**: Check for numerical issues in the model, such as large and small coefficients,
-empty constraints, non-convex quadratic functions.
-
-2. **Feasibility Analysis**: Given an optimized model, or a candidate solution, check if the solutions is
-feasible and optimal (when possible). This includes checking the feasibility of the primal model and
-also the dual model (if available). Complementary slackness conditions are also checked (if applicable).
-
-3. **Infeasibility Analysis**: Given an unsolved of solved model, three steps are made to check for
-    infeasibility:
-    - Check bounds, integers and binaries consistency is also checked at this point.
-    - Propagate bounds in constraints individually, to check if each constraint is infeasible
-    given the current variable bounds. This is only done if bounds are ok.
-    - Run an IIS (Irreducible Inconsistent Subsystem / irreducible infeasible sets)
-    resolver algorithm to find a minimal infeasible subset of constraints.
-    This is only done if no issues are found in the previous two steps.
+ 1. **Numerical Analysis**: Check for numerical issues in the model, such as
+    large and small coefficients, empty constraints, non-convex quadratic
+    functions.
+ 2. **Feasibility Analysis**: Given an optimized model, or a candidate solution,
+    check if the solutions is feasible and optimal (when possible). This
+    includes checking the feasibility of the primal model and also the dual
+    model (if available). Complementary slackness conditions are also checked
+    (if applicable).
+ 3. **Infeasibility Analysis**: Given an unsolved of solved model, three steps
+    are made to check for infeasibility:
+    - Check bounds, integers and binaries consistency is also checked at this
+      point.
+    - Propagate bounds in constraints individually, to check if each constraint
+      is infeasible given the current variable bounds. This is only done if
+      bounds are ok.
+    - Run an IIS (Irreducible Inconsistent Subsystem / irreducible infeasible
+      sets) resolver algorithm to find a minimal infeasible subset of
+      constraints. This is only done if no issues are found in the previous two
+      steps.
 
 ## Installation
 
-You can install the package using the Julia package manager. In the Julia REPL, run:
+You can install the package using the Julia package manager. In the Julia REPL,
+run:
 
 ```julia
 using Pkg
@@ -84,14 +90,16 @@ ModelAnalyzer.summarize(data)
 
 The `ModelAnalyzer.analyze(...)` function can always take the keyword arguments:
  * `verbose = false` to condense the print output.
- * `max_issues = n` to limit the maximum number of issues to report for each type.
+ * `max_issues = n` to limit the maximum number of issues to report for each
+   type.
 
-For certain analysis modes, the `summarize` function can take additional arguments.
+For certain analysis modes, the `summarize` function can take additional
+arguments.
 
 ### Advanced usage
 
-After any `ModelAnalyzer.analyze(...)` call is performed, the resulting data structure
-can be summarized using `ModelAnalyzer.summarize(data)` as show above,
+After any `ModelAnalyzer.analyze(...)` call is performed, the resulting data
+structure can be summarized using `ModelAnalyzer.summarize(data)` as show above,
 or it can be further inspected programmatically.
 
 ```julia

src/feasibility.jl

@@ -38,15 +38,15 @@ julia> data = ModelAnalyzer.analyze(
 
 The additional parameters:
 - `primal_point`: The primal solution point to use for feasibility checking.
-If `nothing`, it will use the current primal solution from optimized model.
+  If `nothing`, it will use the current primal solution from optimized model.
 - `dual_point`: The dual solution point to use for feasibility checking.
-If `nothing` and the model can be dualized, it will use the current dual
-solution from the model.
+  If `nothing` and the model can be dualized, it will use the current dual
+  solution from the model.
 - `atol`: The absolute tolerance for feasibility checking.
 - `skip_missing`: If `true`, constraints with missing variables in the provided
-point will be ignored.
+  point will be ignored.
 - `dual_check`: If `true`, it will perform dual feasibility checking. Disabling
-the dual check will also disable complementarity checking.
+  the dual check will also disable complementarity checking.
 """
 struct Analyzer <: ModelAnalyzer.AbstractAnalyzer end
 
@@ -62,6 +62,11 @@ abstract type AbstractFeasibilityIssue <: ModelAnalyzer.AbstractIssue end
 
 The `PrimalViolation` issue is identified when a primal constraint has a
 left-hand-side value that is not within the constraint's set.
+
+For more information, run:
+```julia
+julia> ModelAnalyzer.summarize(ModelAnalyzer.Feasibility.PrimalViolation)
+```
 """
 struct PrimalViolation <: AbstractFeasibilityIssue
     ref::JuMP.ConstraintRef
@@ -73,6 +78,11 @@ end
 
 The `DualViolation` issue is identified when a constraint has a dual value
 that is not within the dual constraint's set.
+
+For more information, run:
+```julia
+julia> ModelAnalyzer.summarize(ModelAnalyzer.Feasibility.DualViolation)
+```
 """
 struct DualViolation <: AbstractFeasibilityIssue
     ref::Union{JuMP.ConstraintRef,JuMP.VariableRef}
@@ -86,6 +96,11 @@ The `ComplemetarityViolation` issue is identified when a pair of primal
 constraint and dual variable has a nonzero complementarity value, i.e., the
 inner product of the primal constraint's slack and the dual variable's
 violation is not zero.
+
+For more information, run:
+```julia
+julia> ModelAnalyzer.summarize(ModelAnalyzer.Feasibility.ComplemetarityViolation)
+```
 """
 struct ComplemetarityViolation <: AbstractFeasibilityIssue
     ref::JuMP.ConstraintRef
@@ -98,6 +113,11 @@ end
 The `DualObjectiveMismatch` issue is identified when the dual objective value
 computed from problem data and the dual solution does not match the solver's
 dual objective value.
+
+For more information, run:
+```julia
+julia> ModelAnalyzer.summarize(ModelAnalyzer.Feasibility.DualObjectiveMismatch)
+```
 """
 struct DualObjectiveMismatch <: AbstractFeasibilityIssue
     obj::Float64
@@ -110,6 +130,11 @@ end
 The `PrimalObjectiveMismatch` issue is identified when the primal objective
 value computed from problem data and the primal solution does not match
 the solver's primal objective value.
+
+For more information, run:
+```julia
+julia> ModelAnalyzer.summarize(ModelAnalyzer.Feasibility.PrimalObjectiveMismatch)
+```
 """
 struct PrimalObjectiveMismatch <: AbstractFeasibilityIssue
     obj::Float64
@@ -122,6 +147,11 @@ end
 The `PrimalDualMismatch` issue is identified when the primal objective value
 computed from problem data and the primal solution does not match the dual
 objective value computed from problem data and the dual solution.
+
+For more information, run:
+```julia
+julia> ModelAnalyzer.summarize(ModelAnalyzer.Feasibility.PrimalDualMismatch)
+```
 """
 struct PrimalDualMismatch <: AbstractFeasibilityIssue
     primal::Float64
@@ -134,6 +164,11 @@ end
 The `PrimalDualSolverMismatch` issue is identified when the primal objective
 value reported by the solver does not match the dual objective value reported
 by the solver.
+
+For more information, run:
+```julia
+julia> ModelAnalyzer.summarize(ModelAnalyzer.Feasibility.PrimalDualSolverMismatch)
+```
 """
 struct PrimalDualSolverMismatch <: AbstractFeasibilityIssue
     primal::Float64

src/infeasibility.jl

@@ -43,6 +43,11 @@ abstract type AbstractInfeasibilitylIssue <: ModelAnalyzer.AbstractIssue end
 
 The `InfeasibleBounds` issue is identified when a variable has a lower bound
 that is greater than its upper bound.
+
+For more information, run:
+```julia
+julia> ModelAnalyzer.summarize(ModelAnalyzer.Infeasibility.InfeasibleBounds)
+````
 """
 struct InfeasibleBounds{T} <: AbstractInfeasibilitylIssue
     variable::JuMP.VariableRef
@@ -57,6 +62,13 @@ The `InfeasibleIntegrality` issue is identified when a variable has an
 integrality constraint (like `MOI.Integer` or `MOI.ZeroOne`) that is not 
 consistent with its bounds. That is, the bounds do not allow for any
 integer value to be feasible.
+
+For more information, run:
+```julia
+julia> ModelAnalyzer.summarize(
+    ModelAnalyzer.Infeasibility.InfeasibleIntegrality
+)
+```
 """
 struct InfeasibleIntegrality{T} <: AbstractInfeasibilitylIssue
     variable::JuMP.VariableRef
@@ -74,6 +86,13 @@ constraint at a time and all variable bounds of variables involved in the
 constraint.
 This issue can only be found is all variable bounds are consistent, that is,
 no issues of type `InfeasibleBounds` were found in the first layer of analysis.
+
+For more information, run:
+```julia
+julia> ModelAnalyzer.summarize(
+    ModelAnalyzer.Infeasibility.InfeasibleConstraintRange
+)
+```
 """
 struct InfeasibleConstraintRange{T} <: AbstractInfeasibilitylIssue
     constraint::JuMP.ConstraintRef
@@ -90,6 +109,13 @@ constraints cannot be satisfied simultaneously. This is typically found
 by the IIS resolver after the first two layers of infeasibility analysis
 have been completed with no issues, that is, no issues of any other type
 were found.
+
+For more information, run:
+```julia
+julia> ModelAnalyzer.summarize(
+    ModelAnalyzer.Infeasibility.IrreducibleInfeasibleSubset
+)
+```
 """
 struct IrreducibleInfeasibleSubset <: AbstractInfeasibilitylIssue
     constraint::Vector{JuMP.ConstraintRef}