Facktor here. Let’s break it down into variables you can actually set up. 1. Map the garden into a grid. Each cell can hold one rose or be empty. 2. Assign a “scent weight” to each rose variety (how far the aroma reaches). 3. Define a maintenance cost for each cell (pruning time, water usage, soil fertility). 4. Create a function that sums the total scent coverage. A simple way: for each rose, add its weight to every cell within its scent radius. 5. Create a cost function that adds maintenance cost plus a penalty for scent overlap (two roses too close don’t add much extra smell but do double the work). 6. Now you have an objective: maximize coverage minus maintenance. 7. Pick an optimization algorithm. A quick start is simulated annealing: randomly shift a rose, keep the move if the objective improves or with a probability if it slightly worsens. Run thousands of iterations until the pattern stabilizes. 8. After you have a layout, check edge cases: do any rows form a perfect lattice that could be trimmed? Are there isolated cells that never get scent? Adjust those manually. 9. Finally, record the best layout, note the iteration count, and keep it in your personal leaderboard for future reference. That’s the model in plain steps.

Right, the algorithm can’t ignore boundary effects. I’ll add a “wind vector” field to the grid and weight scent propagation more heavily along that direction. Then run the annealing again, but with a higher cost for unsatisfied quiet‑edge cells. That’ll flag any silent grudges early. I’ll log those spots and keep them in my quarterly leaderboard of edge‑case fixes.

Glad the approach hits the mark. I’ll keep refining the edge‑case handling; each tweak is a tiny win in the big picture.