pseudo-code might look like: function matmul(a::AbstractMatrix, b::AbstractMatrix) op = (ai, bi) -> ai * bi + ai * biCHAPTER 12. METHODS 163 ## this is insufficient because it assumes `one(eltype(a))` `+` 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 * biCHAPTER 12. METHODS 163 ## this is insufficient because it assumes `one(eltype(a))` `+` 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)CHAPTER 13. METHODS 172 # this is wrong, since depending on the return value # of type-inference length. The p-norm is defined as ∥A∥p = � n � i=1 |ai|p �1/pCHAPTER 79. LINEAR ALGEBRA 1517 with ai the entries of A, |ai| the norm of ai, and n the length of A. Since the p-norm is computed using

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)CHAPTER 13. METHODS 172 # this is wrong, since depending on the return value # of type-inference length. The p-norm is defined as ∥A∥p = � n � i=1 |ai|p �1/pCHAPTER 79. LINEAR ALGEBRA 1517 with ai the entries of A, |ai| the norm of ai, and n the length of A. Since the p-norm is computed using

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)CHAPTER 13. METHODS 172 # this is wrong, since depending on the return value # of type-inference length. The p-norm is defined as ∥A∥p = � n � i=1 |ai|p �1/pCHAPTER 79. LINEAR ALGEBRA 1517 with ai the entries of A, |ai| the norm of ai, and n the length of A. Since the p-norm is computed using

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 `+` 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 `+` 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)CHAPTER 13. METHODS 173 # this is wrong, since depending on the return value # of type-inference 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