pezkuwichain
5a48ce4498
Uncomment dev-dependencies that were previously commented out to break circular dependencies. These dependencies are needed for tests to compile when running clippy with --all-targets --all-features. Changes include: - pezkuwi/node/*: Add pezkuwi-node-subsystem-test-helpers, pezkuwi-primitives-test-helpers - pezkuwi/xcm/*: Add pezpallet-xcm, xcm-pez-simulator - pezcumulus/client/*: Add test client, runtime, and sproof builder deps - bizinikiwi/pezframe/*: Add bizinikiwi-test-utils, pezframe-support-test, pezpallet-transaction-payment, pezpallet-example-basic, etc. - vendor/pezkuwi-zombienet-sdk: Fix imports from zombienet_sdk to pezkuwi_zombienet_sdk All runtime-benchmarks feature flags have been updated accordingly. cargo clippy --all-targets --all-features --workspace now passes.
pezsp-npos-elections
A set of election algorithms to be used with a Bizinikiwi runtime, typically within the staking sub-system. Notable implementation include:
- [
seq_phragmen]: Implements the Phragmén Sequential Method. An un-ranked, relatively fast election method that ensures PJR, but does not provide a constant factor approximation of the maximin problem. - [
phragmms]: Implements a hybrid approach inspired by Phragmén which is executed faster but it can achieve a constant factor approximation of the maximin problem, similar to that of the MMS algorithm. - [
balance_solution]: Implements the star balancing algorithm. This iterative process can push a solution toward being morebalanced, which in turn can increase its score.
Terminology
This crate uses context-independent words, not to be confused with staking. This is because the election algorithms of this crate, while designed for staking, can be used in other contexts as well.
Voter: The entity casting some votes to a number of Targets. This is the same as Nominator in the context of
staking. Target: The entities eligible to be voted upon. This is the same as Validator in the context of staking.
Edge: A mapping from a Voter to a Target.
The goal of an election algorithm is to provide an ElectionResult. A data composed of:
winners: A flat list of identifiers belonging to those who have won the election, usually ordered in some meaningful way. They are zipped with their total backing stake.assignment: A mapping from each voter to their winner-only targets, zipped with a ration denoting the amount of support given to that particular target.
// the winners.
let winners = vec![(1, 100), (2, 50)];
let assignments = vec![
// A voter, giving equal backing to both 1 and 2.
Assignment {
who: 10,
distribution: vec![(1, Perbill::from_percent(50)), (2, Perbill::from_percent(50))],
},
// A voter, Only backing 1.
Assignment { who: 20, distribution: vec![(1, Perbill::from_percent(100))] },
];
// the combination of the two makes the election result.
let election_result = ElectionResult { winners, assignments };
The Assignment field of the election result is voter-major, i.e. it is from the perspective of the voter. The struct
that represents the opposite is called a Support. This struct is usually accessed in a map-like manner, i.e. keyed by
voters, therefore it is stored as a mapping called SupportMap.
Moreover, the support is built from absolute backing values, not ratios like the example above. A struct similar to
Assignment that has stake value instead of ratios is called an StakedAssignment.
More information can be found at: https://arxiv.org/abs/2004.12990
License: Apache-2.0