## Brute force approaches

- Rect feasibility = number of empty spaces into which the rect fits
- Rect difficulty = 1 / rect feasibility
- Space feasibility = number of remaining input rectangles that fit into the space
	- How to break ties for huge, initial spaces?
		- How much one can insert until current rects AABB is expanded.
			- The more one can insert, the more feasible the space is.
		- In practice, will there be many ties?
			- There shouldn't be many not be if the max_size is carefully chosen
		- Determine difficulty recursively?
			- e.g. sum all successful insertions and successful insertions after each successful insertion...
				- ...quickly becomes exponential
		- Minimum of all free dimensions generated by all insertion trials?
		- Or some coefficient like pathological mult for rectangles?
- Space difficulty = 1 / space feasibility
- Algorithm: until no more spaces or no more rectangles
	- Find the most difficult space
	- among rects that fit...
		- insert the one that generates least spaces or the most difficult space - this means that, into the most difficult space, the most difficult rect will be inserted
			- In case of a perfectly fitting rect, this will be chosen
			- in case of a partly fitting or a strictly smaller rect, insert the most difficult rect, e.g. one that leaves the most difficult spaces
				- however, when splitting due to the rect being strictly smaller, split in the direction that generates a maximally feasible space
- complexity: we iterate every space, number of which will grow linearly as time progresses, and then we iterate each rect 

## Old thoughts

- what about just resizing when there is no more space left?
	- then iterate over all empty spaces and resize those that are touching the edge
		- there might be none like this, though
	- then we can ditch iterating orders
	- we could then easily make it an on-line algorithm