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fix LSR1Operator when s and y are collinear #235

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fix LSR1Operator when s and y are collinear #235

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9 changes: 9 additions & 0 deletions src/lsr1.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -136,6 +136,13 @@ function push!(op::LSR1Operator, s::AbstractVector, y::AbstractVector)
ϵ = eps(eltype(op))
well_defined = abs(dot(ymBs, s)) ≥ ϵ + ϵ * norm(ymBs) * sNorm

# check if y and s are collinear: make a componentwise division and check if the resulting components get approximately the same factor.
and(bool1, bool2) = bool1 && bool2
s_y = (s ./ y)
collinear = (length(s) == 1) || mapreduce(i-> i ≈ s_y[1], and, s_y) # ≈ to avoid numerical issues
collinear && (tmp_scaling = data.scaling) # save data.scaling
collinear && (data.scaling = false) # if the scaling is applied then all the components of Matrix(op_updated) are NaN

sufficient_curvature = true
scaling_condition = true
if data.scaling
Expand Down Expand Up @@ -179,6 +186,8 @@ function push!(op::LSR1Operator, s::AbstractVector, y::AbstractVector)
end
end

collinear && (data.scaling = tmp_scaling) # set data.scaling to its original value

return op
end

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10 changes: 10 additions & 0 deletions test/test_lsr1.jl
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Expand Up @@ -110,6 +110,16 @@ function test_lsr1()
resid = vals[end]
@test norm(resid) ≤ sqrt(eps(eltype(B)))
end

@testset "LSR1 (s,y) collinear" begin
n = 10
lsr1 = LSR1Operator(n)
s = rand(n)
alpha = rand(1) * 1000
y = s .* alpha
push!(lsr1, s, y)
Matrix(lsr1) != I
end
end

test_lsr1()