Open Startonix opened 1 month ago
We can perform optimizations by ensuring efficient usage of resources and fine-tuning mathematical operations.
import numpy as np
def optimized_tensor_product(A, B):
result = np.empty((A.shape[0], B.shape[1]), dtype=A.dtype) np.tensordot(A, B, axes=0, out=result) return result
class CoreMathOperations: @staticmethod def tensor_product(A, B): return optimized_tensor_product(A, B)
We can perform optimizations by ensuring efficient usage of resources and fine-tuning mathematical operations.
Example optimization: using in-place operations and pre-allocated arrays
import numpy as np
def optimized_tensor_product(A, B):
Using in-place operations to save memory and improve speed
class CoreMathOperations: @staticmethod def tensor_product(A, B): return optimized_tensor_product(A, B)
Continue with other optimizations...