Convex jl

  • What is the difference between convex JL and JuMP?

    Convex. jl converts problems to a standard conic form.
    This approach requires (and certifies) that the problem is convex and DCP compliant, and guarantees global optimality of the resulting solution.
    JuMP allows nonlinear programming through an interface that learns about functions via their derivatives..

Convex.jl is a Julia package for Disciplined Convex Programming (DCP). Convex.jl makes it easy to describe optimization problems in a natural, mathematical syntax, and to solve those problems using a variety of different (commercial and open-source) solvers. Convex.jl can solve. linear programs.

Does convex JL have a solver?

By default, Convex jl does not install any solvers

Many users use the solver SCS, which is able to solve problems with linear, second-order cone constraints (SOCPs), exponential constraints and semidefinite constraints (SDPs)

Likewise, COSMO is a pure-Julia solver which can handle every cone that Convex

jl itself supports

How do I fix a convex problem in Julia?

Start by making some small pull requests to fix any of the open issues

Convex jl is a Julia package for Disciplined Convex Programming Convex jl can solve linear programs, mixed-integer linear programs, and DCP-compliant convex programs using a variety of solvers, including Mosek, Gurobi, ECOS, SCS, and GLPK, through MathOptInterface

How does convex work?

To solve this problem, Convex pools all user assets together so that it can purchase curve tokens, convert them into veCRV and maximize rewards for its liquidity providers

This allows Convex users to receive Curve rewards without locking up curve tokens for lengthy periods


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