Raise the LSQR penalty cap to 1e6

src/minimal_covers.jl

@@ -1806,9 +1806,9 @@ function _abslog2_auglag(sys::SupportSystem{T}, x0;
     # Absolute thresholds preserve covariance under rescaling because the
     # residuals are logarithmic.
     vtol = tol === nothing ? 1000 * eps(T) : T(tol)
-    # Precision bounds the usable penalty: the cap is `1e5` (LSQR) or `1e8`
+    # Precision bounds the usable penalty: the cap is `1e6` (LSQR) or `1e8`
     # (exact solves) in `Float64` and grows as `1/sqrt(eps(T))`.
-    κcap = T(use_lsqr ? 1e5 : 1e8) * sqrt(T(eps(Float64)) / eps(T))
+    κcap = T(use_lsqr ? 1e6 : 1e8) * sqrt(T(eps(Float64)) / eps(T))
     # Slack below the inner solver's resolution is indistinguishable from an
     # active entry.
     zdrain = sqrt(eps(T))

test/minimal_covers.jl

@@ -582,7 +582,9 @@ end
     # cover. With either preconditioner it certifies the minimizer, which is also
     # the minimizer of the asymmetric problem. (Whether the full multiplier
     # iteration converges on its own here depends on rounding, so the test
-    # does not rely on it.)
+    # does not rely on it. When the asymmetric solve does stop at the multiplier
+    # iteration's KKT tolerance, its scaled entries are within about 1e-10 of the
+    # certified minimizer rather than at roundoff.)
     an, sn = @test_logs (:warn, r"Increase `maxouter` or `κ`") MatrixCovers._symcover_min_abslog2(A; flopbudget=Inf, maxouter=3,
                                                                                                     polish=false)
     @test !sn.converged
@@ -592,10 +594,10 @@ end
         ap, sp = MatrixCovers._symcover_min_abslog2(A; flopbudget=budget, maxouter=3)
         @test sp.converged && sp.polish.certified
         @test cover_objective(AbsLog{2}(), ap, ap, A) <= cover_objective(AbsLog{2}(), an, an, A)
-        @test ap[Ai] .* ap[Aj] ≈ ga[Ai] .* gb[Aj] rtol=1e-12
+        @test ap[Ai] .* ap[Aj] ≈ ga[Ai] .* gb[Aj] rtol=1e-9
         @test cover_objective(AbsLog{2}(), ap, ap, A) ≈ cover_objective(AbsLog{2}(), ga, gb, A) rtol=1e-12
     end
-    @test af[Ai] .* af[Aj] ≈ ga[Ai] .* gb[Aj] rtol=1e-12
+    @test af[Ai] .* af[Aj] ≈ ga[Ai] .* gb[Aj] rtol=1e-9
 
     # The forest factor needs no budget: any budget below the full factor's
     # gives the forest regime for every solve, and the cover is unchanged.
@@ -884,7 +886,7 @@ end
     # The LSQR path caps escalation where its accuracy floor makes larger
     # weights useless.
     _, sl = MatrixCovers._symcover_min_abslog2(A; linsolve=:lsqr)
-    @test sl.κs[end] <= 1e5
+    @test sl.κs[end] <= 1e6
     @test sl.kkt[end] < 1e-8
 
     # Stopping far short of convergence without the active-set finish emits a warning.
@@ -930,14 +932,14 @@ end
 end
 
 @testset "MMC :lsqr drains slack multipliers at the penalty cap" begin
-    # Draining these multipliers needs a penalty above the `:lsqr` cap of 1e5.
+    # Draining these multipliers needs a penalty above the `:lsqr` cap of 1e6.
     # With the penalty held at the cap they are zeroed directly; left to drain by
-    # `2(κ-1)z` per update they hold the KKT residual at 2.6e-6.
+    # `2(κ-1)z` per update they hold the KKT residual at 6.2e-8.
     # Without the active-set finish (`polish=false`), slack multipliers are zeroed.
     # Whether the residual then reaches the tolerance depends on rounding (with the
     # forest preconditioner it can stall just above it); the solve warns exactly
     # when it does not. The default solve's finish certifies the result.
-    A = banded_sparse_sym(StableRNG(5), 10_000, 3, 1.0)
+    A = banded_sparse_sym(StableRNG(1), 10_000, 3, 1.0)
     for fillbudget in (MatrixCovers.LSQR_FILL_BUDGET, 0)
         (a, s), logs = capture_logs() do
             MatrixCovers._symcover_min_abslog2(A; linsolve=:lsqr, fillbudget, polish=false)
@@ -946,11 +948,11 @@ end
         @test all(l -> l.level == Base.CoreLogging.Warn && occursin("try a larger `κ`", l.message), logs)
         @test s.precond === (fillbudget == 0 ? :forest : :factor)
         @test s.nzeroed > 0
-        @test maximum(s.κs) == 1e5
+        @test maximum(s.κs) == 1e6
         @test s.kkt[end] < 1e-9
         _, sp = @test_logs symcover_min(AbsLog{2}(), A; fillbudget)
         @test MatrixCovers.converged(sp)
-        @test cover_objective(AbsLog{2}(), a, A) ≈ 161350.449205 rtol=1e-9
+        @test cover_objective(AbsLog{2}(), a, A) ≈ 163679.149704 rtol=1e-9
         @test iscover(a, A)
     end