The Basics

cvx_begin and cvx_end

All CVX models must be preceded by the command cvx_begin and terminated with the command cvx_end. All variable declarations, objective functions, and constraints should fall in between. The cvx_begin command may include one more more modifiers:

cvx_begin quiet
Prevents the model from producing any screen output while it is being solved.
cvx_begin sdp
Invokes semidefinite programming mode.
cvx_begin gp
Invokes geometric programming mode.

These modifiers may be combined when appropriate; for instance, cvx_begin sdp quiet invokes SDP mode and silences the solver output.


All variables must be declared using the variable command (or variables command; see below) before they can be used in constraints or an objective function. A variable command includes the name of the variable, an optional dimension list, and one or more keywords that provide additional information about the content or structure of the variable.

Variables can be real or complex scalars, vectors, matrices, or \(n\)-dimensional arrays. For instance,

variable X
variable Y(20,10)
variable Z(5,5,5)

declares a total of 326 (scalar) variables: a scalar X, a 20x10 matrix Y (containing 200 scalar variables), and a 5x5x5 array Z (containing 125 scalar variables).

Variable declarations can also include one or more keywords to denote various structures or conditions on the variable. For instance, to declare a complex variable, use the complex keyword:

variable w(50) complex

Nonnegative variables and symmetric/Hermitian positive semidefinite (PSD) matrices can be specified with the nonnegative and semidefinite keywords, respectively:

variable x(10) nonnegative
variable Z(5,5) semidefinite
variable Q(5,5) complex semidefinite

In this example, x is a nonnegative vector, and Z is a real symmetric PSD matrix and Q``is a complex Hermitian PSD matrix. As we will see below, ``hermitian semidefinite would be an equivalent choice for this third case.

For MIDCPs, the integer and binary keywords are used to declare integer and binary variables, respectively:

variable p(10) integer
variable q binary

A variety of keywords are available to help construct variables with matrix structure such as symmetry or bandedness. For example, the code segment

variable Y(50,50) symmetric
variable Z(100,100) hermitian toeplitz

declares Y to be a real \(50 \times 50\) symmetric matrix variable, and Z a \(100 \times 100\) Hermitian Toeplitz matrix variable. (Note that the hermitian keyword also specifies that the matrix is complex.) The currently supported structure keywords are:

banded(lb,ub)      diagonal           hankel             hermitian
skew_symmetric     symmetric          toeplitz           tridiagonal
lower_bidiagonal   lower_hessenberg   lower_triangular
upper_bidiagonal   upper_hankel       upper_hessenberg   upper_triangular

The underscores can actually be omitted; so, for example, lower triangular is acceptable as well. These keywords are self-explanatory with a couple of exceptions:

the matrix is banded with a lower bandwidth lb and an upper bandwidth ub. If both lb and ub are zero, then a diagonal matrix results. ub can be omitted, in which case it is set equal to lb. For example, banded(1,1) (or banded(1)) is a tridiagonal matrix.
The matrix is Hankel (i.e., constant along antidiagonals), and zero below the central antidiagonal, i.e., for \(i+j>n+1\).

When multiple keywords are supplied, the resulting matrix structure is determined by intersection. For example, symmetric tridiagonal is a valid combination. That said, CVX does reject combinations such as symmetric lower_triangular when a more reasonable alternative exists—diagonal, in this case. Furthermore, if the keywords fully conflict, such that emph{no} non-zero matrix that satisfies all keywords, an error will result.

Matrix-specific keywords can be applied to \(n\)-dimensional arrays as well: each 2-dimensional “slice” of the array is given the stated structure. So for instance, the declaration

variable R(10,10,8) hermitian semidefinite

constructs 8 \(10\times 10\) complex Hermitian PSD matrices, stored in the 2-D slices of R.

As flexible as the variable statement may be, it can only be used to declare a single variable, which can be inconvenient if you have a lot of variables to declare. For this reason, the variables statement is provided which allows you to declare multiple variables; i.e.,

variables x1 x2 x3 y1(10) y2(10,10,10);

The one limitation of the variables command is that it cannot declare complex, integer, or structured variables. These must be declared one at a time, using the singular variable command.

Objective functions

Declaring an objective function requires the use of the minimize or maximize function, as appropriate. (For the benefit of our users whose English favors it, the synonyms minimise and maximise are provided as well.) The objective function in a call to minimize must be convex; the objective function in a call to maximize must be concave; for instance:

minimize( norm( x, 1 ) )
maximize( geo_mean( x ) )

At most one objective function may be declared in a CVX specification, and it must have a scalar value.

If no objective function is specified, the problem is interpreted as a feasibility problem, which is the same as performing a minimization with the objective function set to zero. In this case, cvx_optval is either 0, if a feasible point is found, or +Inf, if the constraints are not feasible.


The following constraint types are supported in CVX:

  • Equality == constraints, where both the left- and right-hand sides are affine expressions.
  • Less-than <= inequality constraints, where the left-hand expression is convex, and the right-hand expression is concave.
  • Greater-than >= constraints, where the left-hand expression is concave, and the right-hand expression is convex.

The non-equality operator ~= may never be used in a constraint; in any case, such constraints are rarely convex. The latest version of CVX now allows you to chain inequalities together; e.g., l <= x <= u. (Previous versions did not allow chained inequalities.)

Note the important distinction between the single equals =, which is an assignment, and the double equals ==, which denotes equality; for more on this distinction, see Assignment and expression holders below.

Strict inequalities < and > are accepted as well, but they are interpreted identically to their nonstrict counterparts. We strongly discourage their use, and a future version of CVX may remove them altogether. For the reasoning behind this, please see the fuller discussion in Strict inequalities.

Inequality and equality constraints are applied in an elementwise fashion, matching the behavior of MATLAB itself. For instance, if A and B are \(m \times n\) arrays, then A<=B is interpreted as \(mn\) (scalar) inequalities A(i,j)<=B(i,j). When one side or the other is a scalar, that value is replicated; for instance, A>0 is interpreted as A(i,j)>=0.

The elementwise treatment of inequalities is altered in semidefinite programming mode; see that section for more details.

CVX also supports a set membership constraint; see Set membership below.


The base CVX function library includes a variety of convex, concave, and affine functions which accept CVX variables or expressions as arguments. Many are common Matlab functions such as sum, trace, diag, sqrt, max, and min, re-implemented as needed to support CVX; others are new functions not found in Matlab. A complete list of the functions in the base library can be found in Reference guide. It is also possible to add your own new functions; see Adding new functions to the atom library.

An example of a function in the base library is the quadratic-over-linear function quad_over_lin:

\[\begin{split}f:\mathbf{R}^{n}\times\mathbf{R} \rightarrow \mathbf{R}, \quad f(x,y) = \begin{cases} x^T x / y & y > 0 \\ +\infty & y \leq 0 \end{cases}\end{split}\]

(The function also accepts complex \(x\), but we’ll consider real \(x\) to keep things simple.) The quadratic-over-linear function is convex in \(x\) and \(y\), and so can be used as an objective, in an appropriate constraint, or in a more complicated expression. We can, for example, minimize the quadratic-over-linear function of \((Ax-b,c^Tx+d)\) using

minimize( quad_over_lin( A * x - b, c' * x + d ) );

inside a CVX specification, assuming x is a vector optimization variable, A is a matrix, b and c are vectors, and d is a scalar. CVX recognizes this objective expression as a convex function, since it is the composition of a convex function (the quadratic-over-linear function) with an affine function.

You can also use the function quad_over_lin outside a CVX specification. In this case, it just computes its (numerical) value, given (numerical) arguments. If c'*x+d is positive, then the result is numerically equivalent tp

( ( A * x - b )' * ( A * x - b ) ) / ( c' * x + d )

However, the quad_over_lin function also performs a domain check, so it returns Inf if c'*x+d is zero or negative.

Set membership

CVX supports the definition and use of convex sets. The base library includes the cone of positive semidefinite \(n \times n\) matrices, the second-order or Lorentz cone, and various norm balls. A complete list of sets supplied in the base library is given in Sets.

Unfortunately, the Matlab language does not have a set membership operator, such as x in S, to denote \(x \in S\). So in CVX, we use a slightly different syntax to require that an expression is in a set. To represent a set we use a function that returns an unnamed variable that is required to be in the set. Consider, for example, \(\mathbf{S}^n_+\), the cone of symmetric positive semidefinite \(n \times n\) matrices. In CVX, we represent this by the function semidefinite(n), which returns an unnamed new variable, that is constrained to be positive semidefinite. To require that the matrix expression X be symmetric positive semidefinite, we use the syntax

X == semidefinite(n)

The literal meaning of this is that X is constrained to be equal to some unnamed variable, which is required to be an \(n \times n\) symmetric positive semidefinite matrix. This is, of course, equivalent to saying that X must itself be symmetric positive semidefinite.

As an example, consider the constraint that a (matrix) variable X is a correlation matrix, i.e., it is symmetric, has unit diagonal elements, and is positive semidefinite. In CVX we can declare such a variable and impose these constraints using

variable X(n,n) symmetric;
X == semidefinite(n);
diag(X) == 1;

The second line here imposes the constraint that X be positive semidefinite. (You can read “==” here as “is” or “is in”, so the second line can be read as X is positive semidefinite’.) The lefthand side of the third line is a vector containing the diagonal elements of X, whose elements we require to be equal to one.

If this use of equality constraints to represent set membership remains confusing or simply aesthetically displeasing, we have created a “pseudo-operator” <In> that you can use in its place. So, for example, the semidefinite constraint above can be replaced by

X <In> semidefinite(n);

This is exactly equivalent to using the equality constraint operator, but if you find it more pleasing, feel free to use it. Implementing this operator required some Matlab trickery, so don’t expect to be able to use it outside of CVX models.

Sets can be combined in affine expressions, and we can constrain an affine expression to be in a convex set. For example, we can impose a constraint of the form

A*X*A'-X <In> B*semidefinite(n)*B';

where X is an \(n \times n\) symmetric variable matrix, and A and B are \(n \times n\) constant matrices. This constraint requires that \(AXA^T-X=BYB^T\), for some \(Y \in \mathbf{S}^n_+\).

CVX also supports sets whose elements are ordered lists of quantities. As an example, consider the second-order or Lorentz cone,

\[\mathbf{Q}^m = \left\{\, (x,y) \in \mathbf{R}^m\times\mathbf{R}\,~|~\, \| x \|_2 \leq y \,\right\} = \operatorname{\textbf{epi}}\|\cdot\|_2,\]

where \(\operatorname{\textbf{epi}}\) denotes the epigraph of a function. An element of \(\mathbf{Q}^m\) is an ordered list, with two elements: the first is an \(m\)-vector, and the second is a scalar. We can use this cone to express the simple least-squares problem from the section Least squares (in a fairly complicated way) as follows:

\[\begin{split}\begin{array}{ll} \text{minimize} & y \\ \text{subject to} & ( A x - b, y ) \in \mathbf{Q}^m. \end{array}\end{split}\]

CVX uses Matlab’s cell array facility to mimic this notation:

    variables x(n) y;
    minimize( y );
    subject to
        { A*x-b, y } <In> lorentz(m);

The function call lorentz(m) returns an unnamed variable (i.e., a pair consisting of a vector and a scalar variable), constrained to lie in the Lorentz cone of length m. So the constraint in this specification requires that the pair { A*x-b, y } lies in the appropriately-sized Lorentz cone.

Dual variables

When a disciplined convex program is solved, the associated dual problem is also solved. (In this context, the original problem is called the primal problem.) The optimal dual variables, each of which is associated with a constraint in the original problem, give valuable information about the original problem, such as the sensitivities with respect to perturbing the constraints (c.f. Convex Optimization, chapter 5). To get access to the optimal dual variables in CVX, you simply declare them, and associate them with the constraints. Consider, for example, the LP

\[\begin{split}\begin{array}{llcll} \mbox{minimize} & c^Tx \\ \mbox{subject to} & Ax \preceq b, \end{array}\end{split}\]

with variable \(x\in\mathbf{R}^n\), and \(m\) inequality constraints. To associate the dual variable \(y\) with the inequality constraint \(Ax\preceq b\) in this LP, we use the following syntax:

n = size(A,2);
    variable x(n);
    dual variable y;
    minimize( c' * x );
    subject to
        y : A * x <= b;

The line

dual variable y

tells CVX that y will represent the dual variable, and the line

y : A * x <= b;

associates it with the inequality constraint. Notice how the colon : operator is being used in a different manner than in standard Matlab, where it is used to construct numeric sequences like 1:10. This new behavior is in effect only when a dual variable is present, so there should be no confusion or conflict. No dimensions are given for y; they are automatically determined from the constraint with which it is associated. For example, if \(m=20\), typing y at the Matlab command prompt immediately before cvx_end yields

y =
    cvx dual variable (20x1 vector)

It is not necessary to place the dual variable on the left side of the constraint; for example, the line above can also be written in this way:

A * x <= b : y;

In addition, dual variables for inequality constraints will always be nonnegative, which means that the sense of the inequality can be reversed without changing the dual variable’s value; i.e.,

b >= A * x : y;

yields an identical result. For equality constraints, on the other hand, swapping the left- and right- hand sides of an equality constraint will negate the optimal value of the dual variable.

After the cvx_end statement is processed, and assuming the optimization was successful, CVX assigns numerical values to x and y—the optimal primal and dual variable values, respectively. Optimal primal and dual variables for this LP must satisfy the complementary slackness conditions

\[y_i ( b - A x )_i = 0, \quad i=1,\dots,m.\]

You can check this in Matlab with the line

y .* (b-A*x)

which prints out the products of the entries of y and b-A*x, which should be nearly zero. This line must be executed after the cvx_end command (which assigns numerical values to x and y); it will generate an error if it is executed inside the CVX specification, where y and b-A*x are still just abstract expressions.

If the optimization is not successful, because either the problem is infeasible or unbounded, then x and y will have different values. In the unbounded case, x will contain an unbounded direction; i.e., a point \(x\) satisfying

\[c^T x = -1, \quad A x \preceq 0,\]

and y will be filled with NaN values, reflecting the fact that the dual problem is infeasible. In the infeasible case, x is filled with NaN values, while y contains an unbounded dual direction; i.e., a point \(y\) satisfying

\[b^T y = -1, \quad A^T y = 0, \quad y \succeq 0\]

Of course, the precise interpretation of primal and dual points and/or directions depends on the structure of the problem. See references such as Convex Optimization for more on the interpretation of dual information.

CVX also supports the declaration of indexed dual variables. These prove useful when the number of constraints in a model (and, therefore, the number of dual variables) depends upon the parameters themselves. For more information on indexed dual variables, see Indexed dual variables.

Assignment and expression holders

Anyone with experience with C or Matlab understands the difference between the single-equal assignment operator = and the double-equal equality operator ==. This distinction is vitally important in CVX as well, and CVX takes steps to ensure that assignments are not used improperly. For instance, consider the following code snippet:

variable X(n,n) symmetric;
X = semidefinite(n);

At first glance, the statement X = semidefinite(n); may look like it constrains X to be positive semidefinite. But since the assignment operator is used, X is actually overwritten by the anonymous semidefinite variable instead. Fortunately, CVX forbids declared variables from being overwritten in this way; when cvx_end is reached, this model would issue the following error:

??? Error using ==> cvx_end
The following cvx variable(s) have been overwritten:
This is often an indication that an equality constraint was
written with one equals '=' instead of two '=='. The model
must be rewritten before cvx can proceed.

We hope that this check will prevent at least some typographical errors from having frustrating consequences in your models.

Despite this warning, assignments can be genuinely useful, so we encourage their use with appropriate care. For instance, consider the following excerpt:

variables x y
z = 2 * x - y;
square( z ) <= 3;
quad_over_lin( x, z ) <= 1;

The construction z = 2 * x - y is not an equality constraint; it is an assignment. It is storing an intermediate calculation 2 * x - y, which is an affine expression, which is then used later in two different constraints. We call z an expression holder to differentiate it from a formally declared CVX variable.

Often it will be useful to accumulate an array of expressions into a single Matlab variable. Unfortunately, a somewhat technical detail of the Matlab object model can cause problems in such cases. Consider this construction:

variable u(9);
x(1) = 1;
for k = 1 : 9,
    x(k+1) = sqrt( x(k) + u(k) );

This seems reasonable enough: x should be a vector whose first value is 1, and whose subsequent values are concave CVX expressions. But if you try this in a CVX model, Matlab will give you a rather cryptic error:

??? The following error occurred converting from cvx to double:
Error using ==> double
Conversion to double from cvx is not possible.

The reason this occurs is that the Matlab variable x is initialized as a numeric array when the assignment x(1)=1 is made; and Matlab will not permit CVX objects to be subsequently inserted into numeric arrays.

The solution is to explicitly declare x to be an expression holder before assigning values to it. We have provided keywords expression and expressions for just this purpose, for declaring a single or multiple expression holders for future assignment. Once an expression holder has been declared, you may freely insert both numeric and CVX expressions into it. For example, the previous example can be corrected as follows:

variable u(9);
expression x(10);
x(1) = 1;
for k = 1 : 9,
    x(k+1) = sqrt( x(k) + u(k) );

CVX will accept this construction without error. You can then use the concave expressions x(1), ..., x(10) in any appropriate ways; for example, you could maximize x(10).

The differences between a variable object and an expression object are quite significant. A variable object holds an optimization variable, and cannot be overwritten or assigned in the CVX specification. (After solving the problem, however, CVX will overwrite optimization variables with optimal values.) An expression object, on the other hand, is initialized to zero, and should be thought of as a temporary place to store CVX expressions; it can be assigned to, freely re-assigned, and overwritten in a CVX specification.

Of course, as our first example shows, it is not always necessary to declare an expression holder before it is created or used. But doing so provides an extra measure of clarity to models, so we strongly recommend it.

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