Time and space complexity are among the first concepts one gets to learn in computer science theory. It’s about the analysis of the time and space an algorithm uses as a function of its input data. Lower time and memory requirements amount to a ‘better’ algorithm and, thus, to improved performance on data sets of increasing size. Optimizing algorithms with respect to their time and space requirements is essential in virtually all areas. Reducing complexity is the key.

With a little bit of imagination, this concept can be applied to photography. For me, this became apparent recently as I tried to shoot woodlands more intentionally. Here, if the camera is pointed somewhere at random, the frame is filled with a large variety of shapes, colors, and different impressions. However, visually pleasing photos show some kind of order and structure: They reduce the complexity within the space of the frame. They guide the eyes of the viewer. They clearly show the subject. They are easy to understand. Reducing complexity is the key.

The above pictures are neither good examples of reduced complexity, nor of woodland photography. Just some first tries on a long road ahead. But I already have some improved pictures of woodlands in the queue, waiting for their own post. Reduce complexity: in algorithmics, in photography, in life.

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