Feasiblity breakers \ penalty error vectors
Nonlinear example demonstrating the need for penalty feasiblity breakers
Further explanation of this example: 'Xpress NonLinear Reference Manual'
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! XNLP example demonstrating the need for penalty feasiblity breakers ! ! This is an SLP specific example. ! Using Xpress-SLP, this examples solves two problem types that relate to various ! infeasibilities in SLP: ! One originating from steps taken along the linearization ! and one originating from deviation from the linearization ! ! This example demonstrates a particular non-linear optimization concept as related ! to Xpress NonLinear. ! The version of the example is for Xpress 7.5. ! ! (c) 2013 Fair Isaac Corporation ! author: Zsolt Csizmadia model mmxnlp_nlp_duals uses "mmxnlp"; declarations x,y: mpvar end-declarations ! This example is hard to be discussed without the solver logs setparam("xnlp_verbose",1) ! This example presents SLP specific behavour setparam("xnlp_solver",XNLP_SOLVER_XSLP) x is_free y is_free ! There is only one constraint. It is not possible to stay on the surface of ! the nonlinear constraint, but feasiblity is achieved by virtue of ! convergnece x^2+y^2 = 1 minimize(y) writeln("x = ", getsol(x),", y = ",getsol(y)) ! adding another constraint, not even necessarily nonlinear ! it is no longer possible to move along the linearization Additional := nlctr(x + y = 1) ! Note that the previous optimal solution is on this constraint ! This definition demonstrates an explicit type cast in Mosel ! The constraint will still be loaded as linear, but the cast ! will allow its redefinition as a nonlinear constraint minimize(y) writeln("x = ", getsol(x),", y = ",getsol(y)) ! In the previous example the extra linear constraint aided the search ! Replacing it with a more complex nonlienar constrint demonstrates how SLP ! sometimes struggle to stay on the linearizations Additional := sin(x) + sin(y) = 1 minimize(y) end-model
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