Update README.md

README.md

@@ -28,7 +28,7 @@ The *ClassyDancingLinks* algorithm also uses classes to represent the nodes of t
 
 The *StructuredDancingLinks* algorithm is struct-based, and, sort of implements its own memory management. A *node store* manages the links between the struct nodes (links are just indices in the node store array). This algorithm also foregoes simple iterator abstractions to loop over the doubly-linked lists. This algorithm is significantly faster than *ClassyDancingLinks*, requiring about 1.2 ms to find the evil sudoku solution. (An experimental non-recursive implementation of this algorithm reduces the required time even further to about 1 ms or less.)
 
-Both benchmarks measure the performance of the respective algorithms to find the solution for the evil sudoku. Each solution is tested for *correctness*: does the solution comply with the rules of a valid sudoku? This validation is taken care of by the sudoku initializer. Whether the solution is also *complete* (i.e.  have all empty cells in the sudoku been assigned a number), is not handled by default. When we add this completeness test to the benchmarks, the results of both algorithm change spectacularly (for reasons as yet unclear). Perfomance of the class-based implementation. drops to about 10 ms, whereas the struct-based implementation suddenly requires 35 ms.
+Both benchmarks measure the performance of the respective algorithms to find the solution for the evil sudoku. Each solution is tested for *correctness*: does the solution comply with the rules of a valid sudoku? This validation is taken care of by the sudoku initializer. Whether the solution is also *complete* (i.e.  have all empty cells in the sudoku been assigned a number), is not handled by default. When we add this completeness test to the benchmarks, the results of both algorithm change spectacularly (for reasons as yet unclear). Perfomance of the class-based implementation drops to about 10 ms, whereas the struct-based implementation suddenly requires 35 ms.
 
 Implementing a version of the algorithm in Swift that approximates the performance of the Scala solution clearly turned out to be less straightforward than expected.