Update

approximation.md

@@ -4,7 +4,7 @@ Approximating means calculating or representing something with lesser than best
 
 Using approximations however doesn't have to imply decrease in precision of the final result -- approximations very well serve [optimization](optimization.md). E.g. approximate metrics help in [heuristic](heuristic.md) algorithms such as [A*](a_star.md). Another use of approximations in optimization is as a quick preliminary check for the expensive precise algorithms: e.g. using bounding spheres helps speed up collision detection (if bounding spheres of two objects don't collide, we know they can't possibly collide and don't have to expensively check this).
 
-Example of approximations:
+Examples of approximations:
 
 - **[Distances](distance.md)**: instead of expensive **Euclidean** distance (`sqrt(dx^2 + dy^2)`) we may use **Chebyshev** distance (`dx + dy`) or **Taxicab** distance (`max(dx,dy)`).
 - **Engineering approximations** ("guesstimations"): e.g. **sin(x) = x** for "small" values of *x* or **pi = 3** (integer instead of float).
@@ -14,4 +14,5 @@ Example of approximations:
 - **[Ray tracing](ray_tracing.md)** neglects indirect lighting. Computer graphics in general is about approximating the solution of the rendering equation.
 - **Real numbers** are practically always approximated with [floating point](floating_point.md) or [fixed point](fixed_point.md) (rational numbers).
 - **[Numerical methods](numerical.md)** offer generality and typically yield approximate solutions while their precision vs speed can be adjusted via parameters such as number of iterations.
-- **[Taylor series](taylor_series.md)** approximates given mathematical function and can be used to e.g. estimate solutions of [differential equations](differential_equation.md).
+- **[Taylor series](taylor_series.md)** approximates given mathematical function and can be used to e.g. estimate solutions of [differential equations](differential_equation.md).
+- ...