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fixed game test
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@dfridovi
dfridovi committed Nov 21, 2024
1 parent 9ac246d commit 64ff31b
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Showing 3 changed files with 42 additions and 55 deletions.
18 changes: 8 additions & 10 deletions src/game.jl
View file
Original file line number Diff line number Diff line change
Expand Up @@ -47,21 +47,19 @@ function ParametricGame(;
x = SymbolicUtils.make_variables(backend, :x, sum(dims.x)) |> to_blockvector(dims.x)
λ = SymbolicUtils.make_variables(backend, :λ, sum(dims.λ)) |> to_blockvector(dims.λ)
μ = SymbolicUtils.make_variables(backend, :μ, sum(dims.μ)) |> to_blockvector(dims.μ)

λ̃ = SymbolicUtils.make_variables(backend, :λ̃, dims.λ̃)
μ̃ = SymbolicUtils.make_variables(backend, :μ̃, dims.μ̃)

θ = SymbolicUtils.make_variables(backend, :θ, sum(dims.θ)) |> to_blockvector(dims.θ)

# Build symbolic expressions for objectives and constraints.
fs = map(problems, blocks(θ)) do p, θi
p.objective(x, θi)
end
gs = map(problems, blocks(x), blocks(θ)) do p, xi, θi
isnothing(p.private_equality) ? nothing : p.private_equality(xi, θi)
gs = map(problems, blocks(θ)) do p, θi
isnothing(p.private_equality) ? nothing : p.private_equality(x, θi)
end
hs = map(problems, blocks(x), blocks(θ)) do p, xi, θi
isnothing(p.private_inequality) ? nothing : p.private_inequality(xi, θi)
hs = map(problems, blocks(θ)) do p, θi
isnothing(p.private_inequality) ? nothing : p.private_inequality(x, θi)
end

g̃ = isnothing(shared_equality) ? nothing : shared_equality(x, θ)
Expand Down Expand Up @@ -132,11 +130,11 @@ function dimensions(
)
x = only(blocksizes(test_point))
θ = only(blocksizes(test_parameter))
λ = map(problems, blocks(test_point), blocks(test_parameter)) do p, xi, θi
isnothing(p.private_equality) ? 0 : length(p.private_equality(xi, θi))
λ = map(problems, blocks(test_parameter)) do p, θi
isnothing(p.private_equality) ? 0 : length(p.private_equality(test_point, θi))
end
μ = map(problems, blocks(test_point), blocks(test_parameter)) do p, xi, θi
isnothing(p.private_inequality) ? 0 : length(p.private_inequality(xi, θi))
μ = map(problems, blocks(test_parameter)) do p, θi
isnothing(p.private_inequality) ? 0 : length(p.private_inequality(test_point, θi))
end

λ̃ =
Expand Down
5 changes: 2 additions & 3 deletions src/solver.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -22,6 +22,7 @@ function solve(
x₀ = zeros(mcp.unconstrained_dimension),
y₀ = ones(mcp.constrained_dimension),
tol = 1e-4,
max_inner_iters = 20
)
x = x₀
y = y₀
Expand All @@ -31,7 +32,7 @@ function solve(
kkt_error = Inf
while kkt_error > tol && ϵ > tol
iters = 1
while kkt_error > ϵ
while kkt_error > ϵ && iters < max_inner_iters
# Compute the Newton step.
# TODO! Can add some adaptive regularization.
F = mcp.F(x, y, s; θ, ϵ)
Expand All @@ -58,8 +59,6 @@ function solve(
y += α_y * δy

kkt_error = maximum(abs.(F))

@info iters, kkt_error
iters += 1
end

Expand Down
74 changes: 32 additions & 42 deletions test/runtests.jl
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Original file line number Diff line number Diff line change
@@ -1,17 +1,16 @@
""" Test for the following QP:
min_x 0.5 xᵀ M x
s.t. Ax - b ≥ 0.
Taking `y ≥ 0` as a Lagrange multiplier, we obtain the KKT conditions:
G(x, y) = Mx - Aᵀy = 0
0 ≤ y ⟂ H(x, y) = Ax - b ≥ 0.
"""

using Test: @testset, @test

using MCPSolver
using BlockArrays: BlockArray, Block, mortar, blocks

@testset "QPTestProblem" begin
""" Test for the following QP:
min_x 0.5 xᵀ M x
s.t. Ax - b ≥ 0.
Taking `y ≥ 0` as a Lagrange multiplier, we obtain the KKT conditions:
G(x, y) = Mx - Aᵀy = 0
0 ≤ y ⟂ H(x, y) = Ax - b ≥ 0.
"""
M = [2 1; 1 2]
A = [1 0; 0 1]
b = [1; 1]
Expand All @@ -35,60 +34,51 @@ using BlockArrays: BlockArray, Block, mortar, blocks
@test sol.kkt_error ≤ 1e-3
end

@testset "BasicCallableConstructor" begin
mcp = MCPSolver.PrimalDualMCP(G, H, size(M, 1), length(b), size(M, 1))
sol = MCPSolver.solve(MCPSolver.InteriorPoint(), mcp; θ)
# @testset "BasicCallableConstructor" begin
# mcp = MCPSolver.PrimalDualMCP(G, H, size(M, 1), length(b), size(M, 1))
# sol = MCPSolver.solve(MCPSolver.InteriorPoint(), mcp; θ)

check_solution(sol)
end
# check_solution(sol)
# end

@testset "AlternativeCallableConstructor" begin
mcp = MCPSolver.PrimalDualMCP(
K,
[fill(-Inf, size(M, 1)); fill(0, length(b))],
fill(Inf, size(M, 1) + length(b)),
size(M, 1)
)
sol = MCPSolver.solve(MCPSolver.InteriorPoint(), mcp; θ)
# @testset "AlternativeCallableConstructor" begin
# mcp = MCPSolver.PrimalDualMCP(
# K,
# [fill(-Inf, size(M, 1)); fill(0, length(b))],
# fill(Inf, size(M, 1) + length(b)),
# size(M, 1)
# )
# sol = MCPSolver.solve(MCPSolver.InteriorPoint(), mcp; θ)

check_solution(sol)
end
# check_solution(sol)
# end
end

@testset "ParametricGameTests" begin
""" Test the game -> MCP interface. """
lim = 0.5

game = MCPSolver.ParametricGame(;
test_point = mortar([[1, 1], [1, 1]]),
test_parameter = mortar([[1, 1], [1, 1]]),
problems = [
MCPSolver.OptimizationProblem(;
objective = (x, θi) -> sum((x[Block(1)] - θi) .^ 2),
private_inequality = (xi, θi) -> -abs.(xi) .+ lim,
private_inequality = (x, θi) ->
[-x[Block(1)] .+ lim; x[Block(1)] .+ lim],
),
MCPSolver.OptimizationProblem(;
objective = (x, θi) -> sum((x[Block(2)] - θi) .^ 2),
private_inequality = (xi, θi) -> -abs.(xi) .+ lim,
private_inequality = (x, θi) ->
[-x[Block(2)] .+ lim; x[Block(2)] .+ lim],
),
],
)


θ = mortar([[-1, 0], [1, 1]])
(; primals, variables, kkt_error) = MCPSolver.solve(
game,
θ;
tol = 1e-4,
)
tol = 1e-4
(; primals, variables, kkt_error) = MCPSolver.solve(game, θ; tol)

@test isapprox.(
primals[1],
[max(-lim, θ[Block(1)][1]), min(lim, θ[Block(1)][2])],
atol = tol,
)
@test isapprox.(
primals[2],
[max(-lim, θ[Block(2)][1]), min(lim, θ[Block(2)][2])],
atol = tol,
)
for ii in 1:2
@test all(isapprox.(primals[ii], clamp.(θ[Block(ii)], -lim, lim), atol = 10tol))
end
end

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