Neuromorphic mathematical optimization with Lava
November 9, 2023
Full Changelog: https://github.com/lava-nc/lava-optimization/compare/v0.3.0...v0.4.0
Full Changelog: https://github.com/lava-nc/lava-optimization/compare/v0.2.4...v0.3.0
Full Changelog: https://github.com/lava-nc/lava-optimization/compare/v0.2.3...v0.2.4
December 14, 2022
Full Changelog: https://github.com/lava-nc/lava-optimization/compare/v0.2.2...v0.2.3
November 4, 2022
Full Changelog: https://github.com/lava-nc/lava-optimization/compare/v0.2.1...v0.2.2
October 31, 2022
Full Changelog: https://github.com/lava-nc/lava-optimization/compare/v0.2.0...v0.2.1
The lava-optim library version 0.2.0 provides a new quadratic unconstrained binary optimization (QUBO) solver executing on CPU. Loihi 2 support via the Loihi extension for Lava is coming soon.
Full Changelog: https://github.com/lava-nc/lava-optimization/compare/v0.1.2...v0.2.0
Lava Optimization 0.1.2 is a bugfix dot release.
Full Changelog: https://github.com/lava-nc/lava-optimization/compare/v0.1.1...v0.1.2
The BSD-3 licensed lava-optimization library will soon include neuromorphic optimization solvers for linear (LP), quadratic (QP), mixed-integer linear (MILP), mixed-integer quadratic (MIQP) and quadratically constrained quadratic (QCQP) programming, as well as solvers for quadratically unconstrained binary optimization (QUBO) and constraint satisfaction problems (CSP). This first release includes a quadratic programming (QP) solver, following the general design principles of all future solvers in the library. As a first example to demonstrate the basic usage concepts of the solver, we provide a tutorial for solving a LASSO/sparse-coding problem.
This first version is implemented using floating point arithmetic and executes on CPU only. In future releases, we will release additional solvers, with support for Loihi-compatible fixed-point arithmetic on CPU and support for execution on Loihi platforms. The various features and API of the solvers will be described at https://lava-nc.org/optimization.html. In the meantime, we appreciate any feedback on the API design and welcome contributions in areas such as enabling pre-conditioning, pre-solving, heuristics, and meta-heuristics for the solvers where such features are pertinent or interfacing with other packages like PuLP.
Full Changelog: https://github.com/lava-nc/lava-optimization/commits/v0.1.1