Business Profile
Code ConicIP provides a pure Julia implementation of the primal-dual predictor-corrector method for convex optimization, delivering robust, portable, and numerically stable optimization routines with practical examples and integration with Julia tooling.
Developers, data scientists, and machine learning researchers who implement convex optimization problems in Julia; users of Convex.jl and JuMP.
Pure Julia implementation focused on robustness, portability, and numerical stability, with explicit integration into the Julia ecosystem (Convex.jl and JuMP) and a library of practical optimization examples. Related project QSip extends non-smooth optimization with a L-BFGS-style solver (Coming Soon).
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A pure Julia implementation of the primal-dual predictor-corrector method for convex optimization, offering robust, portable, and numerically stable routines with practical examples and ecosystem integrations. Includes related project QSip for non-smooth optimization (Coming Soon).
Developers, data scientists, and ML researchers who work on convex optimization problems in Julia ecosystems.
A robust, portable, and numerically stable convex optimization solver built in Julia with tight integration to Convex.jl and JuMP, plus practical examples and a related non-smooth optimization extension.
Julia programming language; Convex.jl; JuMP
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