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Warning Some features may not work without JavaScript. Please try enabling it if you encounter problems. Search PyPI Search. Latest version Released: May 4, Simulated Annealing using tqdm. Navigation Project description Release history Download files. Project links Homepage. Statistics View statistics for this project via Libraries. Maintainers whendrik. Project description Project details Release history Download files Project description Simulated Annealing package for Python using tqdm Flat structure no class definition needed to describe problem.

Using tqdm for progress statistics. Return statistics of used neighbour functions. Installation pip install frigidum Dependencies tqdm 3. For some examples, numpy is required. For each proposal, a neighbour is randomly selected equal weights. Usually attempt to prettify or improve solution. Movements proportion M , e. Movements A movement is a when a proposed state is accepted, and the objective function has changed.

Copy'ing of States 3 most important copy methods are included in the annealing module, def copy state : return state. Bag of Tricks for Simulated Annealing The following bag-of-tricks for simulated annealing have sometimes proven to be useful in some cases. Movements-to-set-Temperature - Use the movements statistics to set the starting and stopping temperature. Neighbours-have-Impact - Focus on the neighbour function, not the cooling scheme or acceptance variations. Objective-has-Impact - The objective, or proxy for objective, also can have major impact on the annealing.

Human-inspired-Neighbours - To get inspiration for random neighbours, try solve a similar problem yourself. Neighbour-Combinations - Try multiple neighbours together, combinations usually work well. The neighbours argument expects a list of neighbours. After a run, statistics are displayed. Don't discard a neighbour just by its statistics, it might be a catalyst for a different neighbours.

Coarse-Neighbours - Try add neighbours, that might work well when warm large simple steps. Local-Opt-Neighbours - Try add neighbours, that find a hyper local minima with local greedy algorithm. Brazen-Overwrite-Neighbours - Try add neighbours, that overwrite a part of the solution rigorously.

Going back from an infeasible solution to feasible can be difficult. Prevent-Optimize-Both-Objective-and-Condition - Only anneal on either condition or objective, not both at the same time. Often, a condition can be constructed as a penalty objective which have to become 0.