#Algorithms

Big O Notation is a spell that hides an algorithm’s efficiency behind the mask of a math formula, inspiring dread of only approximate runtimes. It is an altar to the monster called growth rate, where constant factors are almost always sacrificed. Programmers brandish it to prove their intellect while worshipping complexity. In academia it is revered as sacred truth, yet in practice it often loses its magic depending on the elusive constants. Ultimately, it is worshipped as a one-size-fits-all cage imprisoning every algorithm in a single time-bound orb.

Convex optimization is the alchemy of mathematics boasting to solve every global problem with a single-stroke path. In reality it locks you in the cage of “convex functions”, ignoring any inconvenient curves. The path to the optimum is a one-way street that leaves no room for getting lost, regardless of who tries it. Yet the real world is full of nonconvex traps, casting a cold stare at such casual assumptions. Promising efficiency and guarantees, it is nonetheless a devil in a sweet ideal skin, evoking deep sighs from weary engineers.

Memoization is a function’s art of self-preservation, remembering past results to avoid the torment of recomputation. A magical scheme to escape endless calculations, yet lurking beneath it lies the dread of an ever-growing cache. Reused values become the crown of honor for functions; for developers, a vanity instrument to showcase their brilliance. It boasts efficiency even as one drowns in a deluge of memory—it is efficiency’s cruel paradox.