ImperialCollegeLondon / ImperialCollegeLondon/SWMManywhere
Iterative optimization approach
Open
Nobody has claimed this yet.
feature
- Dominant language
- Python
- Stars
- 51
- Forks
- 11
- PR merge metrics
- No merged PRs in 30d
Description
Suppose it makes sense that parameters could be globally optimized for feasibility, cost and depth. Though I'm in no great rush to do this.
However, adding global feasibility, cost and depth to `metrics` would be easy and sensible.
Contributor guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Start by locating the implementation of `metrics` and how feasibility, cost, and depth parameters are currently handled. Clarify the intended iterative or global optimization approach, then define completion as exposing global feasibility, cost, and depth in `metrics` with coverage for the chosen behavior.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- backend
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100