documenta�on for Julia 1.2-DEV. Work in progress! This documenta�on is for an unreleased, in-development, version of Julia. Please read the release notes to see what has changed since the last release pseudo-code might look like: function matmul(a::AbstractMatrix, b::AbstractMatrix) op = (ai, bi) -> ai * bi + ai * bi ## this is insufficient because it assumes `one(eltype(a))` is constructable: # R = that `+` calls `promote_type` ## but this is not true for some types, such as Bool: # R = promote_type(ai, bi) # this is wrong, since depending on the return value # of type-inference is very brittle (as

pseudo-code might look like: function matmul(a::AbstractMatrix, b::AbstractMatrix) op = (ai, bi) -> ai * bi + ai * bi ## this is insufficient because it assumes `one(eltype(a))` is constructable: # R = that `+` calls `promote_type` ## but this is not true for some types, such as Bool: # R = promote_type(ai, bi) # this is wrong, since depending on the return value # of type-inference is very brittle (as these and to create a single unique instance of others. It is some�mes helpful during module development to turn off incremental precompila�on. The command line flag --compiled-modules={yes|no} enables

documenta�on for Julia 1.3-alpha. Work in progress! This documenta�on is for an unreleased, in-development, version of Julia. Please read the release notes to see what has changed since the last release pseudo-code might look like: function matmul(a::AbstractMatrix, b::AbstractMatrix) op = (ai, bi) -> ai * bi + ai * bi ## this is insufficient because it assumes `one(eltype(a))` is constructable: # R = that `+` calls `promote_type` ## but this is not true for some types, such as Bool: # R = promote_type(ai, bi) # this is wrong, since depending on the return value # of type-inference is very brittle (as

documentation for Julia 1.7-DEV. Work in progress! This documentation is for an unreleased, in-development, version of Julia. Please read the release notes to see what has changed since the last release pseudo-code might look like: function matmul(a::AbstractMatrix, b::AbstractMatrix) op = (ai, bi) -> ai * bi + ai * bi ## this is insufficient because it assumes `one(eltype(a))` is constructable: # R some types, such as Bool: 12.6. PARAMETRICALLY-CONSTRAINED VARARGS METHODS 147 # R = promote_type(ai, bi) # this is wrong, since depending on the return value # of type-inference is very brittle (as

documentation for Julia 1.6-DEV. Work in progress! This documentation is for an unreleased, in-development, version of Julia. Please read the release notes to see what has changed since the last release pseudo-code might look like: function matmul(a::AbstractMatrix, b::AbstractMatrix) op = (ai, bi) -> ai * bi + ai * bi ## this is insufficient because it assumes `one(eltype(a))` is constructable: # R some types, such as Bool: 12.6. PARAMETRICALLY-CONSTRAINED VARARGS METHODS 145 # R = promote_type(ai, bi) # this is wrong, since depending on the return value # of type-inference is very brittle (as

documentation for Julia 1.5-DEV. Work in progress! This documentation is for an unreleased, in-development, version of Julia. Please read the release notes to see what has changed since the last release pseudo-code might look like: function matmul(a::AbstractMatrix, b::AbstractMatrix) op = (ai, bi) -> ai * bi + ai * bi ## this is insufficient because it assumes `one(eltype(a))` is constructable: # R `+` calls `promote_type` ## but this is not true for some types, such as Bool: # R = promote_type(ai, bi) # this is wrong, since depending on the return value # of type-inference is very brittle (as

documenta�on for Julia 1.3-DEV. Work in progress! This documenta�on is for an unreleased, in-development, version of Julia. Please read the release notes to see what has changed since the last release pseudo-code might look like: function matmul(a::AbstractMatrix, b::AbstractMatrix) op = (ai, bi) -> ai * bi + ai * bi ## this is insufficient because it assumes `one(eltype(a))` is constructable: # R = that `+` calls `promote_type` ## but this is not true for some types, such as Bool: # R = promote_type(ai, bi) # this is wrong, since depending on the return value # of type-inference is very brittle (as

pseudo-code might look like: function matmul(a::AbstractMatrix, b::AbstractMatrix) op = (ai, bi) -> ai * bi + ai * bi ## this is insufficient because it assumes `one(eltype(a))` is constructable: # R = that `+` calls `promote_type` ## but this is not true for some types, such as Bool: # R = promote_type(ai, bi) # this is wrong, since depending on the return value # of type-inference is very brittle (as these and to create a single unique instance of others. It is some�mes helpful during module development to turn off incremental precompila�on. The command line flag --compiled-modules={yes|no} enables

pseudo-code might look like: function matmul(a::AbstractMatrix, b::AbstractMatrix) op = (ai, bi) -> ai * bi + ai * bi ## this is insufficient because it assumes `one(eltype(a))` is constructable: # R = that `+` calls `promote_type` ## but this is not true for some types, such as Bool: # R = promote_type(ai, bi) # this is wrong, since depending on the return value # of type-inference is very brittle (as these and to create a single unique instance of others. It is some�mes helpful during module development to turn off incremental precompila�on. The command line flag --compiled-modules={yes|no} enables

pseudo-code might look like: function matmul(a::AbstractMatrix, b::AbstractMatrix) op = (ai, bi) -> ai * bi + ai * bi ## this is insufficient because it assumes `one(eltype(a))` is constructable: # R `+` calls `promote_type` ## but this is not true for some types, such as Bool: # R = promote_type(ai, bi) # this is wrong, since depending on the return value # of type-inference is very brittle (as these and to create a single unique instance of others. It is sometimes helpful during module development to turn off incremental precompilation. The command line flag --compiled-modules={yes|no} enables