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Add binary broadcasting support and LinearAlgebra.norm for JuMP array expressions, then use them in the neural.jl example to minimize the L2 loss between network output and target data. https://claude.ai/code/session_01GWT1QHA3D5BpMQBEHvgbcV
Test all three binary broadcasting dispatches (JuMPArray-Array, Array-JuMPArray, JuMPArray-JuMPArray), LinearAlgebra.norm on array expressions, and the full L2 loss pipeline from the neural example. https://claude.ai/code/session_01GWT1QHA3D5BpMQBEHvgbcV
Required for the `import LinearAlgebra` in src/JuMP/operators.jl to extend `LinearAlgebra.norm` for array expressions. https://claude.ai/code/session_01GWT1QHA3D5BpMQBEHvgbcV
JuMP's standard @objective cannot convert ArrayDiff's GenericArrayExpr into MOI format. The example and test now construct the L2 loss expression without setting it as a JuMP objective. https://claude.ai/code/session_01GWT1QHA3D5BpMQBEHvgbcV
JuMP defines `LinearAlgebra.norm(::AbstractArray{<:AbstractJuMPScalar})`
which throws UnsupportedNonlinearOperator. Our AbstractJuMPArray types
have elements that are AbstractJuMPScalar, causing ambiguity. Constrain
both the container and element type to resolve it.
https://claude.ai/code/session_01GWT1QHA3D5BpMQBEHvgbcV
Define LinearAlgebra.norm for GenericArrayExpr and ArrayOfVariables
separately instead of for the abstract AbstractJuMPArray type. This
avoids any dispatch ambiguity with JuMP's error-throwing norm method
for AbstractArray{<:AbstractJuMPScalar}.
https://claude.ai/code/session_01GWT1QHA3D5BpMQBEHvgbcV
Codecov Report✅ All modified and coverable lines are covered by tests. Additional details and impacted files@@ Coverage Diff @@
## main #38 +/- ##
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+ Coverage 90.82% 90.92% +0.09%
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Files 20 20
Lines 2377 2391 +14
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+ Hits 2159 2174 +15
+ Misses 218 217 -1 ☔ View full report in Codecov by Sentry. 🚀 New features to boost your workflow:
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JuMP's GenericNonlinearExpr constructor validates arguments via _is_real(). GenericArrayExpr was missing this method, causing norm() to fail when wrapping array expressions in NonlinearExpr. Also consolidate the L2 loss tests into a single comprehensive test. https://claude.ai/code/session_01GWT1QHA3D5BpMQBEHvgbcV
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Add binary broadcasting support and LinearAlgebra.norm for JuMP array
expressions, then use them in the neural.jl example to minimize the L2
loss between network output and target data.
https://claude.ai/code/session_01GWT1QHA3D5BpMQBEHvgbcV