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. \)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. CVX: Matlab Software for Disciplined Convex Programming
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