fix(optimization): hrp tolerates asymmetric correlations (v0.5.1)

CHANGELOG.md

@@ -5,6 +5,16 @@ All notable changes to `RiskLabAI.jl` are documented here. The format is based o
 [Semantic Versioning](https://semver.org/spec/v2.0.0.html) (pre-1.0: minor
 versions may include breaking changes).
 
+## [0.5.1] — 2026-06-19
+
+### Fixed
+
+- `Optimization.hrp` now symmetrises the correlation-distance matrix before
+  single-linkage clustering, so it accepts correlation matrices with the tiny
+  floating-point asymmetry that real sample covariances always have (previously
+  `Clustering.hclust` threw `ArgumentError: Distance matrix should be symmetric`).
+  Added a regression test covering an asymmetric correlation input.
+
 ## [0.5.0] — 2026-06-19
 
 First substantive release. The package was reconstructed from the ground up to
@@ -67,5 +77,6 @@ machine-learning pieces are validated structurally. The parity ledger lives in
 
 - Initial package skeleton.
 
+[0.5.1]: https://github.com/RiskLabAI/RiskLabAI.jl/releases/tag/v0.5.1
 [0.5.0]: https://github.com/RiskLabAI/RiskLabAI.jl/releases/tag/v0.5.0
 [0.0.1]: https://github.com/RiskLabAI/RiskLabAI.jl/releases/tag/v0.0.1

Project.toml

@@ -1,6 +1,6 @@
 name = "RiskLabAI"
 uuid = "a72881da-fdaa-49c1-8962-99caf4ccfee8"
-version = "0.5.0"
+version = "0.5.1"
 authors = ["RiskLab AI <research@risklab.ai>"]
 
 [deps]

src/Optimization/HierarchicalRiskParity.jl

@@ -131,6 +131,7 @@ function hrp(
     correlation::AbstractMatrix{<:Real},
 )
     distance = distance_corr(correlation)
+    distance = (distance .+ distance') ./ 2          # enforce exact symmetry for hclust
     order = hclust(distance; linkage = :single).order
     bisection = recursive_bisection(covariance, order)
     weights = zeros(Float64, length(order))

test/runtests.jl

@@ -940,6 +940,18 @@ end
     @test length(w_hrp) == 4
     @test sum(w_hrp) ≈ 1.0
     @test all(w_hrp .> 0)
+
+    # Regression: hrp() must accept a correlation with floating-point asymmetry
+    # (real sample covariances are not bit-exactly symmetric → hclust would throw
+    # "Distance matrix should be symmetric" without the symmetrisation guard).
+    g = randn(MersenneTwister(7), 6, 6)
+    psd = g * g'
+    sym_corr = RiskLabAI.Cluster.covariance_to_correlation(psd)
+    asym_corr = copy(sym_corr)
+    asym_corr[1, 2] += 1e-12                       # break exact symmetry, as FP does
+    w_sample = O.hrp(psd, asym_corr)
+    @test length(w_sample) == 6
+    @test sum(w_sample) ≈ 1.0
 end
 
 @testset "Validation — cross-validators (parity with Python)" begin