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 p-norm is defined as 998 CHAPTER 81. LINEAR ALGEBRA ∥A∥p = � n � i=1 |ai|p �1/p with ai the entries of A, |ai| the norm of ai, and n the length of A. Since the p-norm is computed using the norms of

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 the corresponding length. The p-norm is defined as ∥A∥p = � n � i=1 |ai|p �1/p with ai the entries of A, |ai| the norm of ai, and n the length of A. Since the p-norm is computed using the norms of

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 the corresponding length. The p-norm is defined as ∥A∥p = � n � i=1 |ai|p �1/p with ai the entries of A, |ai| the norm of ai, and n the length of A. Since the p-norm is computed using the norms of

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 p-norm is defined as 1000 CHAPTER 82. LINEAR ALGEBRA ∥A∥p = � n � i=1 |ai|p �1/p with ai the entries of A, |ai| the norm of ai, and n the length of A. Since the p-norm is computed using the norms of

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 the corresponding length. The p-norm is defined as ∥A∥p = � n � i=1 |ai|p �1/p with ai the entries of A, |ai| the norm of ai, and n the length of A. Since the p-norm is computed using the norms of

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 the corresponding length. The p-norm is defined as ∥A∥p = � n � i=1 |ai|p �1/p with ai the entries of A, |ai| the norm of ai, and n the length of A. Since the p-norm is computed using the norms of

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 the corresponding length. The p-norm is defined as ∥A∥p = � n � i=1 |ai|p �1/p with ai the entries of A, |ai| the norm of ai, and n the length of A. Since the p-norm is computed using the norms of

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 ## but this is not true for some types, such as Bool: CHAPTER 12. METHODS 138 # R = promote_type(ai, bi) # this is wrong, since depending on the return value # of type-inference is very brittle (as the corresponding length. The p-norm is defined as ∥A∥p = � n � i=1 |ai|p �1/p with ai the entries of A, |ai| the norm of ai, and n the length of A. Since the p-norm is computed using the norms of

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 ## but this is not true for some types, such as Bool: CHAPTER 12. METHODS 138 # R = promote_type(ai, bi) # this is wrong, since depending on the return value # of type-inference is very brittle (as the corresponding length. The p-norm is defined as ∥A∥p = � n � i=1 |ai|p �1/p with ai the entries of A, |ai| the norm of ai, and n the length of A. Since the p-norm is computed using the norms of

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 ## but this is not true for some types, such as Bool: CHAPTER 12. METHODS 138 # R = promote_type(ai, bi) # this is wrong, since depending on the return value # of type-inference is very brittle (as the corresponding length. The p-norm is defined as ∥A∥p = � n � i=1 |ai|p �1/p with ai the entries of A, |ai| the norm of ai, and n the length of A. Since the p-norm is computed using the norms of