I have dealt with the pain of annoying solver interfaces in the past and just seeing all this come together so cleanly and effortlessly for the user is a huge differentiator for Julia.
Amazing work Madeleine, and the whole JuliaOpt team. I continue to cheer from the sidelines. Perhaps some blog posts on the whole solver interoperability would be great to have. -viral On Thursday, February 5, 2015 at 8:42:33 AM UTC+5:30, Miles Lubin wrote: > > I'm personally very pleased to see this. The JuMP team has worked closely > with the Convex.jl team to make sure that we share a common infrastructure > (through MathProgBase) to talk to solvers, and I don't think it's an > exaggeration to say that this has resulted in an unprecedented level of > solver interoperability. At this point I'm hard pressed to think of another > platform besides Julia which lets you easily switch between AMPL/GAMS-style > modeling (via JuMP) and DCP-style modeling (via Convex.jl) as you might > experiment with different formulations of a problem. > > On Wednesday, February 4, 2015 at 8:57:51 PM UTC-5, Elliot Saba wrote: >> >> This is so so cool, Madeleine. Thank you for sharing. I'm a huge fan of >> DCP, ever since I took a convex optimization course here at the UW (which >> of course featured cvx and Boyd's book) and seeing this in Julia makes me >> smile. >> -E >> >> On Wed, Feb 4, 2015 at 5:53 PM, Madeleine Udell <madelei...@gmail.com> >> wrote: >> >>> Convex.jl <https://github.com/JuliaOpt/Convex.jl> is a Julia library >>> for mathematical programming that makes it easy to formulate and fast to >>> solve nonlinear convex optimization problems. Convex.jl >>> <https://github.com/JuliaOpt/Convex.jl> is a member of the JuliaOpt >>> <https://github.com/JuliaOpt> umbrella group and can use (nearly) any >>> solver that complies with the MathProgBase interface, including Mosek >>> <https://github.com/JuliaOpt/Mosek.jl>, Gurobi >>> <https://github.com/JuliaOpt/gurobi.jl>, ECOS >>> <https://github.com/JuliaOpt/ECOS.jl>, SCS >>> <https://github.com/JuliaOpt/SCS.jl>, and GLPK >>> <https://github.com/JuliaOpt/GLPK.jl>. >>> >>> Here's a quick example of code that solves a non-negative least-squares >>> problem. >>> >>> using Convex >>> >>> # Generate random problem data >>> m = 4; n = 5 >>> A = randn(m, n); b = randn(m, 1) >>> >>> # Create a (column vector) variable of size n x 1. >>> x = Variable(n) >>> >>> # The problem is to minimize ||Ax - b||^2 subject to x >= 0 >>> problem = minimize(sum_squares(A * x + b), [x >= 0]) >>> >>> solve!(problem) >>> >>> We could instead solve a robust approximation problem by replacing >>> sum_squares(A >>> * x + b) by sum(norm(A * x + b, 1)) or sum(huber(A * x + b)); it's that >>> easy. >>> >>> Convex.jl <https://github.com/JuliaOpt/Convex.jl> is different from JuMP >>> <https://github.com/JuliaOpt/JuMP.jl> in that it allows (and >>> prioritizes) linear algebraic and functional constructions in objectives >>> and constraints (like max(x,y) < A*z). Under the hood, it converts >>> problems to a standard conic form, which requires (and certifies) that the >>> problem is convex, and guarantees global optimality of the resulting >>> solution. >>> >> >>
