Package evaluation of AlgebraicDecisionDiagrams on Julia 1.11.4 (a71dd056e0*) started at 2025-04-08T06:59:57.622


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# Set-up
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Installing PkgEval dependencies (TestEnv)...

Set-up completed after 8.88s


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# Installation
#

Installing AlgebraicDecisionDiagrams...
   Resolving package versions...
    Updating `~/.julia/environments/v1.11/Project.toml`
  [fda02c4b] + AlgebraicDecisionDiagrams v0.1.0
    Updating `~/.julia/environments/v1.11/Manifest.toml`
  [fda02c4b] + AlgebraicDecisionDiagrams v0.1.0

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# Precompilation
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Precompiling PkgEval dependencies...

Precompiling package dependencies...

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# Testing
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     Testing AlgebraicDecisionDiagrams
      Status `/tmp/jl_oXflkQ/Project.toml`
  [fda02c4b] AlgebraicDecisionDiagrams v0.1.0
  [8dfed614] Test v1.11.0
      Status `/tmp/jl_oXflkQ/Manifest.toml`
  [fda02c4b] AlgebraicDecisionDiagrams v0.1.0
  [2a0f44e3] Base64 v1.11.0
  [b77e0a4c] InteractiveUtils v1.11.0
  [56ddb016] Logging v1.11.0
  [d6f4376e] Markdown v1.11.0
  [9a3f8284] Random v1.11.0
  [ea8e919c] SHA v0.7.0
  [9e88b42a] Serialization v1.11.0
  [8dfed614] Test v1.11.0
     Testing Running tests...
l1 = 
@╮ V1 [3] 
 ├─⊂−⊃─(false) [1]
 ╰─⊂+⊃─(true) [2]
¬l1 = 
@╮ V1 [6] 
 ├─⊂−⊃─(true) [4]
 ╰─⊂+⊃─(false) [5]
apply(~, l1) = 
@╮ V1 [3] 
 ├─⊂−⊃─(true) [7]
 ╰─⊂+⊃─(false) [8]
nliteral(1) = 
@╮ V1 [11] 
 ├─⊂−⊃─(true) [9]
 ╰─⊂+⊃─(false) [10]
l1 ∧ ¬l1 = (false) [15]
l1 ∨ ¬l1 = (true) [25]
[ Info: Fig. 7 in Bryant's paper
¬(l1 ∧ l3) ∨ l2 ∧ l3 = 
@╮ V1 [62] 
 ├─⊂−⊃─(true) [55]
 ╰─⊂+⊃─╮ V2 [61] 
       ├─⊂−⊃─╮ V3 [59] 
       │     ├─⊂−⊃─(true) [55]
       │     ╰─⊂+⊃─(false) [58]
       ╰─⊂+⊃─(true) [55]

Test Summary:             | Pass  Total  Time
Boolean Decision Diagrams |    2      2  4.5s
[ Info: ADD
x1 = indicator(Int, 1) = 
@╮ V1 [65] 
 ├─⊂−⊃─(0) [63]
 ╰─⊂+⊃─(1) [64]
one(x1) - x1 = 
@╮ V1 [69] 
 ├─⊂−⊃─(1) [67]
 ╰─⊂+⊃─(0) [68]
¬x1 = 
@╮ V1 [72] 
 ├─⊂−⊃─(1) [70]
 ╰─⊂+⊃─(0) [71]
apply((x->begin
            #= /home/pkgeval/.julia/packages/AlgebraicDecisionDiagrams/R2zvO/test/add.jl:8 =#
            1 - x
        end), x1) = 
@╮ V1 [65] 
 ├─⊂−⊃─(1) [73]
 ╰─⊂+⊃─(0) [74]
x1 + ¬x1 = (1) [78]
x1 * ¬x1 = (0) [83]
x2 = indicator(Int, 2) = 
@╮ V2 [87] 
 ├─⊂−⊃─(0) [85]
 ╰─⊂+⊃─(1) [86]
x1 + x2 = 
@╮ V1 [94] 
 ├─⊂−⊃─╮ V2 [90] 
 │     ├─⊂−⊃─(0) [88]
 │     ╰─⊂+⊃─(1) [89]
 ╰─⊂+⊃─╮ V2 [93] 
       ├─⊂−⊃─(1) [89]
       ╰─⊂+⊃─(2) [92]
x1 * x2 = 
@╮ V1 [100] 
 ├─⊂−⊃─(0) [95]
 ╰─⊂+⊃─╮ V2 [99] 
       ├─⊂−⊃─(0) [95]
       ╰─⊂+⊃─(1) [98]
apply((x->begin
            #= /home/pkgeval/.julia/packages/AlgebraicDecisionDiagrams/R2zvO/test/add.jl:14 =#
            x ^ 2
        end), x1 + x2) = 
@╮ V1 [107] 
 ├─⊂−⊃─╮ V2 [103] 
 │     ├─⊂−⊃─(0) [108]
 │     ╰─⊂+⊃─(1) [109]
 ╰─⊂+⊃─╮ V2 [106] 
       ├─⊂−⊃─(1) [109]
       ╰─⊂+⊃─(4) [111]
x ⊕ y = begin
        #= /home/pkgeval/.julia/packages/AlgebraicDecisionDiagrams/R2zvO/test/add.jl:15 =#
        (x + y) % 2
    end = var"#⊕#6"()
apply(⊕, x1, x2) = 
@╮ V1 [118] 
 ├─⊂−⊃─╮ V2 [114] 
 │     ├─⊂−⊃─(0) [112]
 │     ╰─⊂+⊃─(1) [113]
 ╰─⊂+⊃─╮ V2 [117] 
       ├─⊂−⊃─(1) [113]
       ╰─⊂+⊃─(0) [112]
x3 = indicator(Int, 3) = 
@╮ V3 [121] 
 ├─⊂−⊃─(0) [119]
 ╰─⊂+⊃─(1) [120]
[ Info: Odd Parity Function
apply(⊕, x1, apply(⊕, x2, x3)) = 
@╮ V1 [137] 
 ├─⊂−⊃─╮ V2 [133] 
 │     ├─⊂−⊃─╮ V3 [131] 
 │     │     ├─⊂−⊃─(0) [129]
 │     │     ╰─⊂+⊃─(1) [130]
 │     ╰─⊂+⊃─╮ V3 [132] 
 │           ├─⊂−⊃─(1) [130]
 │           ╰─⊂+⊃─(0) [129]
 ╰─⊂+⊃─╮ V2 [136] 
       ├─⊂−⊃─╮ V3 [132] 
       │     ├─⊂−⊃─(1) [130]
       │     ╰─⊂+⊃─(0) [129]
       ╰─⊂+⊃─╮ V3 [131] 
             ├─⊂−⊃─(0) [129]
             ╰─⊂+⊃─(1) [130]
apply(max, x1 * x2, x3) = 
@╮ V1 [150] 
 ├─⊂−⊃─╮ V3 [146] 
 │     ├─⊂−⊃─(0) [144]
 │     ╰─⊂+⊃─(1) [145]
 ╰─⊂+⊃─╮ V2 [149] 
       ├─⊂−⊃─╮ V3 [146] 
       │     ├─⊂−⊃─(0) [144]
       │     ╰─⊂+⊃─(1) [145]
       ╰─⊂+⊃─(1) [145]
[ Info: Example from Fig. 1 in Bryant's paper
apply(max, ¬x1 * x2, x1 * x3) = 
@╮ V1 [171] 
 ├─⊂−⊃─╮ V2 [168] 
 │     ├─⊂−⊃─(0) [166]
 │     ╰─⊂+⊃─(1) [167]
 ╰─⊂+⊃─╮ V3 [170] 
       ├─⊂−⊃─(0) [166]
       ╰─⊂+⊃─(1) [167]
[ Info: Example in Fig. 5 of Bryant's paper
A4 = ADD.reduce(Node(1, Node(2, zero(Int), A3), Node(2, A3, A3))) = 
@╮ V1 [178] 
 ├─⊂−⊃─╮ V2 [176] 
 │     ├─⊂−⊃─(0) [175]
 │     ╰─⊂+⊃─╮ V3 [174] 
 │           ├─⊂−⊃─(0) [175]
 │           ╰─⊂+⊃─(1) [173]
 ╰─⊂+⊃─╮ V3 [174] 
       ├─⊂−⊃─(0) [175]
       ╰─⊂+⊃─(1) [173]

[ Info: Directed Graph ADD
[ Info: Example in Fig 1. in Bahar et al. 1997's paper
graph = Terminal(2) * nx0 * nx1 * ny0 * y1 + Terminal(2) * nx0 * nx1 * y0 * ny1 + Terminal(2) * nx0 * x1 * ny0 * y1 + Terminal(2) * nx0 * x1 * y0 * ny1 + Terminal(4) * x0 * nx1 * y0 * y1 + Terminal(4) * x0 * x1 * ny0 * y1 = 
@╮ V0 [398] 
 ├─⊂−⊃─╮ V1 [391] 
 │     ├─⊂−⊃─╮ V3 [389] 
 │     │     ├─⊂−⊃─(0) [387]
 │     │     ╰─⊂+⊃─(2) [388]
 │     ╰─⊂+⊃─╮ V3 [390] 
 │           ├─⊂−⊃─(2) [388]
 │           ╰─⊂+⊃─(0) [387]
 ╰─⊂+⊃─╮ V1 [397] 
       ├─⊂−⊃─╮ V2 [394] 
       │     ├─⊂−⊃─(0) [387]
       │     ╰─⊂+⊃─╮ V3 [393] 
       │           ├─⊂−⊃─(0) [387]
       │           ╰─⊂+⊃─(4) [392]
       ╰─⊂+⊃─╮ V2 [396] 
             ├─⊂−⊃─╮ V3 [393] 
             │     ├─⊂−⊃─(0) [387]
             │     ╰─⊂+⊃─(4) [392]
             ╰─⊂+⊃─(0) [387]
[ Info: Get scope
scope = mapreduce(index, union, nt) = [0, 1, 2, 3]

[ Info: Matrix examples in page 9 of Bryant's paper
f = ¬x0 * ¬y0 + x0 * y0 = 
@╮ V0 [422] 
 ├─⊂−⊃─╮ V1 [419] 
 │     ├─⊂−⊃─(1) [417]
 │     ╰─⊂+⊃─(0) [418]
 ╰─⊂+⊃─╮ V1 [421] 
       ├─⊂−⊃─(0) [418]
       ╰─⊂+⊃─(1) [417]
g = Terminal(4) * nx0 + Terminal(2) * x0 = 
@╮ V0 [433] 
 ├─⊂−⊃─(4) [431]
 ╰─⊂+⊃─(2) [432]
h = f + g = 
@╮ V0 [440] 
 ├─⊂−⊃─╮ V1 [436] 
 │     ├─⊂−⊃─(5) [434]
 │     ╰─⊂+⊃─(4) [435]
 ╰─⊂+⊃─╮ V1 [439] 
       ├─⊂−⊃─(2) [437]
       ╰─⊂+⊃─(3) [438]

[ Info: Restriction
restrict(h, 0, false) = 
@╮ V1 [441] 
 ├─⊂−⊃─(5) [434]
 ╰─⊂+⊃─(4) [435]
h | (1 => true) = 
@╮ V0 [442] 
 ├─⊂−⊃─(4) [435]
 ╰─⊂+⊃─(3) [438]
(h | (index(x0) => true)) + (h | (index(x0) => false)) = (7) [445]
(h | (index(y0) => true)) + (h | (index(y0) => false)) = 
@╮ V0 [455] 
 ├─⊂−⊃─(9) [453]
 ╰─⊂+⊃─(5) [454]

[ Info: marginalization
marginalize(h, index(x0)) = (7) [458]
marginalize(h, index(y0)) = 
@╮ V0 [464] 
 ├─⊂−⊃─(9) [462]
 ╰─⊂+⊃─(5) [463]
A4 = 
@╮ V1 [178] 
 ├─⊂−⊃─╮ V2 [176] 
 │     ├─⊂−⊃─(0) [175]
 │     ╰─⊂+⊃─╮ V3 [174] 
 │           ├─⊂−⊃─(0) [175]
 │           ╰─⊂+⊃─(1) [173]
 ╰─⊂+⊃─╮ V3 [174] 
       ├─⊂−⊃─(0) [175]
       ╰─⊂+⊃─(1) [173]
(A4 | (2 => true)) + (A4 | (2 => false)) = 
@╮ V1 [475] 
 ├─⊂−⊃─╮ V3 [472] 
 │     ├─⊂−⊃─(0) [470]
 │     ╰─⊂+⊃─(1) [471]
 ╰─⊂+⊃─╮ V3 [474] 
       ├─⊂−⊃─(0) [470]
       ╰─⊂+⊃─(2) [473]
A5 = marginalize(A4, 2) = 
@╮ V1 [486] 
 ├─⊂−⊃─╮ V3 [483] 
 │     ├─⊂−⊃─(0) [481]
 │     ╰─⊂+⊃─(1) [482]
 ╰─⊂+⊃─╮ V3 [485] 
       ├─⊂−⊃─(0) [481]
       ╰─⊂+⊃─(2) [484]
Test Summary:               | Pass  Total  Time
Algebraic Decision Diagrams |    2      2  5.3s
x1 = indicator(MLE, 1) = 
@╮ V1 [489] 
 ├─⊂−⊃─(0) [487]
 ╰─⊂+⊃─(1) [488]
x2 = indicator(MLE, 2) = 
@╮ V2 [492] 
 ├─⊂−⊃─(0) [490]
 ╰─⊂+⊃─(1) [491]
x3 = x1 + x2 = 
@╮ V1 [499] 
 ├─⊂−⊃─╮ V2 [495] 
 │     ├─⊂−⊃─(0) [493]
 │     ╰─⊂+⊃─(1) [494]
 ╰─⊂+⊃─╮ V2 [498] 
       ├─⊂−⊃─(1) [494]
       ╰─⊂+⊃─(2) [497]
d1 = Node(1, 1 + MLE(-1.0, 1), MLE(1, 1)) = 
@╮ V1 [502] 
 ├─⊂−⊃─(1 + -1χ1) [500]
 ╰─⊂+⊃─(1χ1) [501]
m1 = x1 + d1 = 
@╮ V1 [505] 
 ├─⊂−⊃─(1 + -1χ1) [503]
 ╰─⊂+⊃─(1 + 1χ1) [504]
d2 = Node(2, 1 + MLE(-1.0, 2), MLE(1, 2)) = 
@╮ V2 [508] 
 ├─⊂−⊃─(1 + -1χ2) [506]
 ╰─⊂+⊃─(1χ2) [507]
m2 = x2 + d2 = 
@╮ V2 [511] 
 ├─⊂−⊃─(1 + -1χ2) [509]
 ╰─⊂+⊃─(1 + 1χ2) [510]
m3 = d1 * d2 = 
@╮ V1 [518] 
 ├─⊂−⊃─╮ V2 [514] 
 │     ├─⊂−⊃─(1 + 1χ1χ2 + -1χ2 + -1χ1) [512]
 │     ╰─⊂+⊃─(-1χ1χ2 + 1χ2) [513]
 ╰─⊂+⊃─╮ V2 [517] 
       ├─⊂−⊃─(-1χ1χ2 + 1χ1) [515]
       ╰─⊂+⊃─(1χ1χ2) [516]
convert(MLE, 1) = 1
promote(MLE(1.0, 2), 2) = (1.0χ2, 2)

[ Info: Example from the paper
v = Dict("a" => -1, "b" => -2) = Dict("b" => -2, "a" => -1)
ϕA = ADD.reduce(Node(0, Node(5, 1 + MLE(-1.0, v["a"]), MLE(1.0, v["a"])), Node(1, Node(5, 1 + MLE(-1.0, v["a"]), MLE(1.0, v["a"])), Node(6, 1 + MLE(-1.0, v["a"]), MLE(1.0, v["a"]))))) = 
@╮ V0 [529] 
 ├─⊂−⊃─╮ V5 [521] 
 │     ├─⊂−⊃─(1 + -1χ-1) [519]
 │     ╰─⊂+⊃─(1.0χ-1) [520]
 ╰─⊂+⊃─╮ V1 [528] 
       ├─⊂−⊃─╮ V5 [521] 
       │     ├─⊂−⊃─(1 + -1χ-1) [519]
       │     ╰─⊂+⊃─(1.0χ-1) [520]
       ╰─⊂+⊃─╮ V6 [527] 
             ├─⊂−⊃─(1 + -1χ-1) [519]
             ╰─⊂+⊃─(1.0χ-1) [520]
ϕ5 = Node(5, MLE(0.6), MLE(0.4)) = 
@╮ V5 [532] 
 ├─⊂−⊃─(0.6) [530]
 ╰─⊂+⊃─(0.4) [531]
μ1 = marginalize(ϕA * ϕ5, 5) = 
@╮ V0 [554] 
 ├─⊂−⊃─(0.6 + -0.19999999999999996χ-1) [549]
 ╰─⊂+⊃─╮ V1 [553] 
       ├─⊂−⊃─(0.6 + -0.19999999999999996χ-1) [549]
       ╰─⊂+⊃─╮ V6 [552] 
             ├─⊂−⊃─(1.0 + -1.0χ-1) [550]
             ╰─⊂+⊃─(1.0χ-1) [551]
μ1p = apply(new_monomial, μ1) = 
@╮ V0 [554] 
 ├─⊂−⊃─(1.0χ1) [555]
 ╰─⊂+⊃─╮ V1 [553] 
       ├─⊂−⊃─(1.0χ1) [555]
       ╰─⊂+⊃─╮ V6 [552] 
             ├─⊂−⊃─(1.0χ2) [557]
             ╰─⊂+⊃─(1.0χ3) [558]
ϕ6 = Node(6, MLE(0.1), MLE(0.9)) = 
@╮ V6 [561] 
 ├─⊂−⊃─(0.1) [559]
 ╰─⊂+⊃─(0.9) [560]
μ2 = marginalize(μ1p * ϕ6, 6) = 
@╮ V0 [577] 
 ├─⊂−⊃─(1.0χ1) [574]
 ╰─⊂+⊃─╮ V1 [576] 
       ├─⊂−⊃─(1.0χ1) [574]
       ╰─⊂+⊃─(0.1χ2 + 0.9χ3) [575]
μ2p = apply(new_monomial, μ2) = 
@╮ V0 [577] 
 ├─⊂−⊃─(1.0χ4) [578]
 ╰─⊂+⊃─╮ V1 [576] 
       ├─⊂−⊃─(1.0χ4) [578]
       ╰─⊂+⊃─(1.0χ5) [580]
ϕ1 = Node(1, MLE(0.625), MLE(0.375)) = 
@╮ V1 [583] 
 ├─⊂−⊃─(0.625) [581]
 ╰─⊂+⊃─(0.375) [582]
μ3 = marginalize(μ2p * ϕ1, 1) = 
@╮ V0 [594] 
 ├─⊂−⊃─(1.0χ4) [592]
 ╰─⊂+⊃─(0.375χ5 + 0.625χ4) [593]
μ3p = apply(new_monomial, μ3) = 
@╮ V0 [594] 
 ├─⊂−⊃─(1.0χ6) [595]
 ╰─⊂+⊃─(1.0χ7) [596]
ϕB = ADD.reduce(Node(0, Node(7, 1 + MLE(-1.0, v["b"]), MLE(1.0, v["b"])), Node(8, 1 + MLE(-1.0, v["b"]), MLE(1.0, v["b"])))) = 
@╮ V0 [603] 
 ├─⊂−⊃─╮ V7 [599] 
 │     ├─⊂−⊃─(1 + -1χ-2) [597]
 │     ╰─⊂+⊃─(1.0χ-2) [598]
 ╰─⊂+⊃─╮ V8 [602] 
       ├─⊂−⊃─(1 + -1χ-2) [597]
       ╰─⊂+⊃─(1.0χ-2) [598]
ϕ8 = Node(8, MLE(0.8), MLE(0.2)) = 
@╮ V8 [606] 
 ├─⊂−⊃─(0.8) [604]
 ╰─⊂+⊃─(0.2) [605]
μ4 = marginalize(μ3p * ϕB * ϕ8, 8) = 
@╮ V0 [633] 
 ├─⊂−⊃─╮ V7 [631] 
 │     ├─⊂−⊃─(1.0χ6 + -1.0χ-2χ6) [629]
 │     ╰─⊂+⊃─(1.0χ-2χ6) [630]
 ╰─⊂+⊃─(-0.6000000000000001χ-2χ7 + 0.8χ7) [632]
μ4p = apply(new_monomial, μ4) = 
@╮ V0 [633] 
 ├─⊂−⊃─╮ V7 [631] 
 │     ├─⊂−⊃─(1.0χ8) [634]
 │     ╰─⊂+⊃─(1.0χ9) [635]
 ╰─⊂+⊃─(1.0χ10) [636]
ϕ0 = Node(0, MLE(0.2), MLE(0.8)) = 
@╮ V0 [639] 
 ├─⊂−⊃─(0.2) [637]
 ╰─⊂+⊃─(0.8) [638]
μ5 = marginalize(μ4p * ϕ0, 0) = 
@╮ V7 [648] 
 ├─⊂−⊃─(0.8χ10 + 0.2χ8) [646]
 ╰─⊂+⊃─(0.8χ10 + 0.2χ9) [647]
μ5p = apply(new_monomial, μ5) = 
@╮ V7 [648] 
 ├─⊂−⊃─(1.0χ11) [649]
 ╰─⊂+⊃─(1.0χ12) [650]
ϕ7 = Node(7, MLE(0.3), MLE(0.7)) = 
@╮ V7 [653] 
 ├─⊂−⊃─(0.3) [651]
 ╰─⊂+⊃─(0.7) [652]
μ6 = marginalize(μ5p * ϕ7, 7) = (0.3χ11 + 0.7χ12) [657]

[ Info: Bilinear program
Maximize: 0.3χ11 + 0.7χ12
Subject to:
1.0χ1 = 0.6 + -0.19999999999999996χ-1
1.0χ2 = 1.0 + -1.0χ-1
1.0χ3 = 1.0χ-1
1.0χ4 = 1.0χ1
1.0χ5 = 0.1χ2 + 0.9χ3
1.0χ6 = 1.0χ4
1.0χ7 = 0.375χ5 + 0.625χ4
1.0χ8 = 1.0χ6 + -1.0χ-2χ6
1.0χ9 = 1.0χ-2χ6
1.0χ10 = -0.6000000000000001χ-2χ7 + 0.8χ7
1.0χ11 = 0.8χ10 + 0.2χ8
1.0χ12 = 0.8χ10 + 0.2χ9
Test Summary:                            | Pass  Total  Time
Parametrized Algebraic Decision Diagrams |    1      1  8.2s
     Testing AlgebraicDecisionDiagrams tests passed 

Testing completed after 22.56s

PkgEval succeeded after 50.79s