More information about CVX can be found in the CVX Users’ Guide, which can be found online in a searchable format, or downloaded as a PDF. If it is neither of these, then CVX is not the correct tool for the task. It is important to confirm that your model can be expressed as an MIDCP or a GP before you begin using CVX. It is not a general-purpose tool for nonlinear optimization, nor is it a tool for checking whether or not your model is convex. It is quite important to also note what CVX is not. Nevertheless, we believe that MIDCP support is a powerful addition to CVX and we look forward to seeing how our users take advantage of it. Not all solvers support MIDCPs, and those that do cannot guarantee a successful solution in reasonable time for all models. It is important to note that MIDCPs are not convex, and most non-convex models cannot be expressed as an MIDCP. Mixed integer DCPs must obey the disciplined convex programming ruleset however, one or more of the variables may be constrained to assume integer or binary values. Version 2.0 of CVX brings support for mixed integer disciplined convex programming (MIDCP). In this mode, CVX allows GPs to be constructed in their native, nonconvex form, transforms them automatically to a solvable convex form, and translates the numerical results back to the original problem. Geometric programs are not convex, but can be made so by applying a certain transformation. For more information on disciplined convex programming, see these resources for the basics of convex analysis and convex optimization, see the book Convex Optimization.ĬVX also supports geometric programming (GP) through the use of a special GP mode. Constraints and objectives that are expressed using these rules are automatically transformed to a canonical form and solved. Under this approach, convex functions and sets are built up from a small set of rules from convex analysis, starting from a base library of convex functions and sets. In its default mode, CVX supports a particular approach to convex optimization that we call disciplined convex programming.
#Matlab optimization toolbox code
\)The following code segment generates and solves a random instance of this model: m = 20 n = 10 p = 4 Ĭ = randn(p,n) d = randn(p,1) e = rand For example, consider the following convex optimization model: CVX turns Matlab into a modeling language, allowing constraints and objectives to be specified using standard Matlab expression syntax. Give it a try!ĬVX is a Matlab-based modeling system for convex optimization. Click here to watch it.ĬVX 3.0 beta: We’ve added some interesting new features for users and system administrators. New: Professor Stephen Boyd recently recorded a video introduction to CVX for Stanford’s convex optimization courses.
#Matlab optimization toolbox software
Is there some intermediate setup step that I am missing in order to make the mapping between the toolbox name in my license and the dynare code?įYI, I am running Dynare 4.6.3 on Matlab R2020b.CVX: Matlab Software for Disciplined Convex Programming
HasLicense=license(‘checkout’,‘GADS_Toolbox’) Indeed, if I evaluate license() using this name, I get The problem seems to arise from the “dynare_minimize_objective” function where the various cases (such as case 12 on line 423) that use the Global Optimization Toolbox call the user-defined function user_has_matlab_license() with the argument “global_optimization_toolbox”, which in that function on line 34 is then used in the Matlab function “ = license(‘checkout’,toolbox) ” as “toolbox”.Īccording to Matlab, the toolbox name must match the name in the user’s Matlab license file, which for the the Global Optimization Toolbox is “GADS_Toolbox”. I confirmed with Matlab tech support that I do indeed have the correct Global Optimization Toolbox license and that the toolbox is working correctly. Option mode_compute=12 requires the Global Optimization Toolbox” “Error using dynare_minimize_objective (line 427) When I try to access the various mode_compute methods that use the Matlab Global Optimization Toolbox (such as mode_compute=12), I receive the error
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