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KNITRO + JuMP: linear --> nonlinear constraints?

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Alan Crawford

KNITRO + JuMP: linear --> nonlinear constraints?

This is probably an old issue - but i wondered why linear constraints are being converted in to nonlinear constraints when called through JuMP?

For reference i am using Julia v0.5 with Knitro 10.0.1. Also

julia> Pkg.status("JuMP")

- JuMP 0.14.1

julia> Pkg.status("KNITRO")

- KNITRO 0.2.0

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Miles Lubin

Re: KNITRO + JuMP: linear --> nonlinear constraints?

Not sure what you mean exactly, but possibly see https://github.com/JuliaOpt/KNITRO.jl/commit/67a6e8379d1b39476282abb56a6e65598e2896c6 which hasn't been tagged yet.

On Wednesday, November 30, 2016 at 10:14:09 AM UTC-5, Alan Crawford wrote:

This is probably an old issue - but i wondered why linear constraints are being converted in to nonlinear constraints when called through JuMP?

For reference i am using Julia v0.5 with Knitro 10.0.1. Also

julia> Pkg.status("JuMP")

- JuMP 0.14.1

julia> Pkg.status("KNITRO")

- KNITRO 0.2.0

Miles Lubin

Re: KNITRO + JuMP: linear --> nonlinear constraints?

If you use that you should update to Knitro 10.1.2 otherwise you may silently get incorrect answers on "rare" occasions.

On Wednesday, November 30, 2016 at 10:16:24 AM UTC-5, Miles Lubin wrote:

Not sure what you mean exactly, but possibly see <a href="https://github.com/JuliaOpt/KNITRO.jl/commit/67a6e8379d1b39476282abb56a6e65598e2896c6" target="_blank" rel="nofollow" onmousedown="this.href='https://www.google.com/url?q\x3dhttps%3A%2F%2Fgithub.com%2FJuliaOpt%2FKNITRO.jl%2Fcommit%2F67a6e8379d1b39476282abb56a6e65598e2896c6\x26sa\x3dD\x26sntz\x3d1\x26usg\x3dAFQjCNGTJozoTyism49lpcxYNRl8bgc74A';return true;" onclick="this.href='https://www.google.com/url?q\x3dhttps%3A%2F%2Fgithub.com%2FJuliaOpt%2FKNITRO.jl%2Fcommit%2F67a6e8379d1b39476282abb56a6e65598e2896c6\x26sa\x3dD\x26sntz\x3d1\x26usg\x3dAFQjCNGTJozoTyism49lpcxYNRl8bgc74A';return true;">https://github.com/JuliaOpt/KNITRO.jl/commit/67a6e8379d1b39476282abb56a6e65598e2896c6 which hasn't been tagged yet.

On Wednesday, November 30, 2016 at 10:14:09 AM UTC-5, Alan Crawford wrote:
This is probably an old issue - but i wondered why linear constraints are being converted in to nonlinear constraints when called through JuMP?

For reference i am using Julia v0.5 with Knitro 10.0.1. Also

julia> Pkg.status("JuMP")

- JuMP 0.14.1

julia> Pkg.status("KNITRO")

- KNITRO 0.2.0

Alan Crawford

Re: KNITRO + JuMP: linear --> nonlinear constraints?

Thanks Miles. To clarify:

In my jump model i get the following summary information:

Minimization problem with:

* 5000 linear constraints

* 3784 variables

Solver is Knitro

Then when i solve the model I get that knitro output telling me i have 1000 nonlinear constraints:

=======================================

Academic License

(NOT FOR COMMERCIAL USE)

Artelys Knitro 10.0.1

=======================================

Knitro presolve eliminated 0 variables and 0 constraints.

outlev: 1

Knitro changing algorithm from AUTO to 1.

Knitro changing bar_initpt from AUTO to 3.

Knitro changing bar_murule from AUTO to 4.

Knitro changing bar_penaltycons from AUTO to 1.

Knitro changing bar_penaltyrule from AUTO to 2.

Knitro changing bar_switchrule from AUTO to 2.

Knitro changing linsolver from AUTO to 5.

Problem Characteristics ( Presolved)

-----------------------

Objective goal: Minimize

Number of variables: 3784 ( 3784)

bounded below: 0 ( 0)

bounded above: 0 ( 0)

bounded below and above: 0 ( 0)

fixed: 0 ( 0)

free: 3784 ( 3784)

Number of constraints: 5000 ( 5000)

linear equalities: 0 ( 0)

nonlinear equalities: 0 ( 0)

linear inequalities: 0 ( 0)

nonlinear inequalities: 5000 ( 5000)

range: 0 ( 0)

Number of nonzeros in Jacobian: 94750 ( 94750)

Number of nonzeros in Hessian: 7161220 ( 7161220)

EXIT: Locally optimal solution found.

Final Statistics

----------------

Final objective value = -3.50504042566882e-01

Final feasibility error (abs / rel) = 9.60e-11 / 9.60e-11

Final optimality error (abs / rel) = 2.64e-10 / 2.64e-10

# of iterations = 19

# of CG iterations = 0

# of function evaluations = 21

# of gradient evaluations = 21

# of Hessian evaluations = 19

Total program time (secs) = 170.44518 ( 170.338 CPU time)

Time spent in evaluations (secs) = 106.45541

===============================================================================

On Wednesday, 30 November 2016 15:18:11 UTC, Miles Lubin wrote:

If you use that you should update to Knitro 10.1.2 otherwise you may silently get incorrect answers on "rare" occasions.

On Wednesday, November 30, 2016 at 10:16:24 AM UTC-5, Miles Lubin wrote:
Not sure what you mean exactly, but possibly see <a href="https://github.com/JuliaOpt/KNITRO.jl/commit/67a6e8379d1b39476282abb56a6e65598e2896c6" rel="nofollow" target="_blank" onmousedown="this.href='https://www.google.com/url?q\x3dhttps%3A%2F%2Fgithub.com%2FJuliaOpt%2FKNITRO.jl%2Fcommit%2F67a6e8379d1b39476282abb56a6e65598e2896c6\x26sa\x3dD\x26sntz\x3d1\x26usg\x3dAFQjCNGTJozoTyism49lpcxYNRl8bgc74A';return true;" onclick="this.href='https://www.google.com/url?q\x3dhttps%3A%2F%2Fgithub.com%2FJuliaOpt%2FKNITRO.jl%2Fcommit%2F67a6e8379d1b39476282abb56a6e65598e2896c6\x26sa\x3dD\x26sntz\x3d1\x26usg\x3dAFQjCNGTJozoTyism49lpcxYNRl8bgc74A';return true;">https://github.com/JuliaOpt/KNITRO.jl/commit/67a6e8379d1b39476282abb56a6e65598e2896c6 which hasn't been tagged yet.

On Wednesday, November 30, 2016 at 10:14:09 AM UTC-5, Alan Crawford wrote:
This is probably an old issue - but i wondered why linear constraints are being converted in to nonlinear constraints when called through JuMP?

For reference i am using Julia v0.5 with Knitro 10.0.1. Also

julia> Pkg.status("JuMP")

- JuMP 0.14.1

julia> Pkg.status("KNITRO")

- KNITRO 0.2.0

Alan Crawford

Re: KNITRO + JuMP: linear --> nonlinear constraints?

In reply to this post by Alan Crawford

I checked out the master of KNITRO.jl and downloaded the KNITRO v 10.1.2, I ran the example from KNITRO.jl

using KNITRO, JuMP
m = Model(solver=KnitroSolver())
@variable(m, x[1:3]>=0)
@objective(m, Min, 9.0 - 8.0*x[1] - 6.0*x[2] - 4.0*x[3]
+ 2.0*x[1]^2 + 2.0*x[2]^2 + x[3]^2
+ 2.0*x[1]*x[2] + 2.0*x[1]*x[3])
@constraint(m, x[1] + x[2] + 2.0*x[3] <= 3)
solve(m)

KNITRO now correctly picks up problem structure.

On Wednesday, 30 November 2016 15:14:09 UTC, Alan Crawford wrote:

This is probably an old issue - but i wondered why linear constraints are being converted in to nonlinear constraints when called through JuMP?

For reference i am using Julia v0.5 with Knitro 10.0.1. Also

julia> Pkg.status("JuMP")

- JuMP 0.14.1

julia> Pkg.status("KNITRO")

- KNITRO 0.2.0