2nd research iteration results & roadmapping
tags: gitcoin
Reports
Authors: Danilo Lessa Bernardineli
Results under the original scope
The definitions & methodology of the research plan were sucessfully implemented
This includes creating rewiring optimizers based on Hill Climbing and Simulated Annealing that were pushed to the NetworkX module
We have the capacity of knowing the Optimality Gap under limited circumstances
The current capacity is limited due to the complexity factor of the current QF algorithm implementation, which is quadratic: \(O(n^2)\)
The further testing of the proposed hypothesis is blocked by further R&D
Large-scale analysis and simulations requires potentially tens of thousands of evaluations, or the adoption of better heuristics. A more comprehensive mathematical description of the algorithm could also create shortcuts.
Results Outside the scope
We've written drafts of mathematical specs that could result in drastic improvements to QF evaluation performance on operations and research.
We've tested the effect of attack vectors under limited subsets of the Gitcoin Grants data.
We've promoted public research interest on Gitcoin and Quadratic Funding.
Improving the scalability of the Optimality Gap calculations
Starting point heuristics based on attack vectors
Iterative rewiring algorithm for QF
Linear Algebra formalism for QF
Researching analytical solutions for optimizing QF
Investigating the effect of attack vectors under a range of scenarios on synthetic / subset scenarios
What if the distribution of the amounts change?
Searching for attack vector patterns on synthetic / subset scenarios
Resume presentation
2nd research iteration results & roadmapping tags: gitcoin Reports Updated by December 2021 Authors: Danilo Lessa Bernardineli
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