Rename some functions.

Author: scheinerman

Project.toml

@@ -1,7 +1,7 @@
 name = "SimplexTableaux"
 uuid = "b49aa546-d643-4829-bb91-bc1e5e539d80"
 authors = ["Ed Scheinerman <ers@jhu.edu>"]
-version = "0.1.5"
+version = "0.2.0"
 
 [deps]
 ChooseOptimizer = "858a232f-1959-5553-8cfc-91e1fd5304e2"

development/examples.jl

@@ -1,7 +1,7 @@
 using SimplexTableaux, SimpleDrawing, Plots, SimpleDrawingObjects, Clines
 
 """
-    dog_food()
+	dog_food()
 
 A,b,c for chapter 3 dogfood problem
 """
@@ -13,7 +13,7 @@ function dog_food()
 end
 
 """
-    peanut_butter()
+	peanut_butter()
 
 A,b,c for peanut butter manufacturing problem in chapter 3
 """
@@ -25,7 +25,7 @@ function peanut_butter()
 end
 
 """
-    fishkind4()
+	fishkind4()
 
 DEF's example for chapter 4.
 """
@@ -37,7 +37,7 @@ function fishkind4()
 end
 
 """
-    fishkind400()
+	fishkind400()
 
 An example that fails the big-M method for small M. 
 """
@@ -49,7 +49,7 @@ function fishkind400()
 end
 
 """
-    small_example()
+	small_example()
 
 Small example for section 4.4
 """
@@ -217,4 +217,28 @@ function phase_one_trouble()
     return Tableau(A, b, c, false)
 end
 
+"""
+	dual_infeasible()
+
+Return an LP that is infeasible and whose dual is infeasible. 
+"""
+function dual_infeasible()
+    A = [-1 -2 0 0; -3 -4 0 0; 0 0 1 2; 0 0 3 4]
+    b = [2, 2, 4, 5]
+    c = [2, 3, -4, -7]
+    return Tableau(A, b, c)
+end
+
+"""
+    small_dual_infeasible()
+
+Return an LP that is infeasible and whose dual is infeasible. 
+"""
+function small_dual_infeasible()
+    A = [-1 0; 0 1]
+    b = [1, 1]
+    c = [1, -1]
+    return Tableau(A, b, c)
+end
+
 nothing

docs/src/other.md

@@ -6,8 +6,8 @@
 * `basic_vector(T)` returns the vector in which the nonbasic variables have been set to zero. 
 * `get_Abc(T)` returns the original matrix `A` and the vectors `b` and `c` for the standard presentation of the linear program.
 * `get_basis(T)` returns the column numbers of the current basis.
-* `is_feasible(T)` returns `true` if the current basic vector is in the feasible region.
-* `is_optimal(T)` returns `true` if the tableau has reached an optimal (minimal) state.
+* `in_feasible_state(T)` returns `true` if the current basic vector is in the feasible region.
+* `in_optimal_state(T)` returns `true` if the tableau has reached an optimal (minimal) state.
 * `value(T)` returns the objective function value of the current basic vector. 
 
 

src/Bases.jl

@@ -64,8 +64,8 @@ function find_all_bases(T::Tableau)
     r, c = size(T.A)
     TT = deepcopy(T)
     result = [
-        B for
-        B in combinations(1:c, r) if check_basis(TT, B) && is_feasible(set_basis!(TT, B))
+        B for B in combinations(1:c, r) if
+        check_basis(TT, B) && in_feasible_state(set_basis!(TT, B))
     ]
 
     # T.M = TT.M

src/SimplexTableaux.jl

@@ -26,9 +26,9 @@ export Tableau,
     find_pivot_column,
     get_Abc,
     get_basis,
+    in_feasible_state,
+    in_optimal_state,
     infer_basis!,
-    is_feasible,
-    is_optimal,
     lp_solve,
     matrix_pivot!,
     basis_pivot!,
@@ -46,22 +46,22 @@ include("Pretty.jl")
 include("Dual.jl")
 
 """
-    is_feasible(T::Tableau)::Bool
+    in_feasible_state(T::Tableau)::Bool
 
 Return `true` is the current state of `T` is at a feasible vector.
 """
-function is_feasible(T::Tableau)::Bool
+function in_feasible_state(T::Tableau)::Bool
     x = basic_vector(T)
     all(x .>= 0)
 end
 
 """
-    is_feasible(T::Tableau, x::Vector)::Bool
+    in_feasible_state(T::Tableau, x::Vector)::Bool
 
 Return `true` is the vector `x` is a in the feasible region
 of the LP represented in `T`.
 """
-function is_feasible(T::Tableau, x::Vector)::Bool
+function in_feasible_state(T::Tableau, x::Vector)::Bool
     return T.A*x == T.b && all(x .>= 0)
 end
 

src/Solver.jl

@@ -106,7 +106,7 @@ function simplex_solve!(T::Tableau, verbose::Bool=true)
 
     pivot_count = 0
 
-    while !is_optimal(T)
+    while !in_optimal_state(T)
         p = find_pivot(T)
         if 0 ∈ p
             if verbose

src/Tableau.jl

@@ -123,6 +123,6 @@ function value(T::Tableau)
     return value(T, x)
 end
 
-function is_optimal(T::Tableau)
+function in_optimal_state(T::Tableau)
     return all(T.M[1, 2:(end - 1)] .≤ 0)
 end

test/runtests.jl

@@ -9,13 +9,13 @@ using SimplexTableaux
     T = Tableau(A, b, c, false)
 
     simplex_solve!(T, false)
-    @test is_feasible(T)
-    @test is_optimal(T)
+    @test in_feasible_state(T)
+    @test in_optimal_state(T)
 
-    @test is_feasible(T)
+    @test in_feasible_state(T)
 
     set_basis!(T, [3, 4])
-    @test !is_feasible(T)
+    @test !in_feasible_state(T)
 end
 
 @testset "Duality" begin