The DCP ruleset

Disciplined convex programming (DCP) requires all models obey a set of rules, or conventions, that govern how expressions and functions can appear in objectives and constraints. These rules, which we call the DCP ruleset, are drawn from basic principles of convex analysis and are relatively easy to learn. But they are not exhaustive, which means that it is possible to construct expressions and models that are known to be convex, but still violate the rules.

To illustrate the difference between convexity and disciplined convexity, consider the function \(f(x)=\sqrt{x^2+1}\). It is simple to prove that this function is convex; its second derivative \(f^{(2)}(x)=(x^2+1)^{-3/2}\) is positive for all \(x\). The ruleset is silent on derivatives, however. And if you attempt to express \(f\) in the obvious manner in CVX, you will see this error:

>> sqrt(x^2+1)
Error using cvx/sqrt (line 61)
Disciplined convex programming error:
    Illegal operation: sqrt( {nonnegative convex} ).

The problem is that the composition rules forbid the application of a concave function (like sqrt) to a convex expression (like x^2+1), since that usually produces a nonconvex result. So CVX rejects the expression on this basis.

Fortunately, most situations like this can be resolved simply by rewriting the expression. In this case, \(f\) can also be written as \(f(x)=\|[x~1]\|_2\); a form which CVX accepts:

>> norm([x 1])
ans =
    cvx nonnegative convex expression (scalar)

This expression is acceptable because norm is among the functions supported by CVX, and it is being used in a manner compliant with the composition rules. So \(f(x)=\sqrt{x^2+1}\) can indeed be used in CVX models, as long as it is expressed in a compliant manner.

At first, the DCP ruleset may seem arbitrary or restrictive, but it serves a very important purpose. Each rule corresponds to a specific step that CVX takes to convert models to solvable form. When CVX rejects a model for a rule violation, then, it is doing so because it does not know how to solve it. Put another way, by complying with the rules, you are not only proving that your model is convex, you are also giving CVX a detailed recipe for solving it. (We thank you for the help!)

In truth, it is never the rules that ultimately prevent a model from being represented in CVX. Rather, it is the finite size of the function library, which is in turn limited by the capabilities of the underlying numerical solvers. Still, the greater your mastery of the DCP ruleset, the more productive you will be.

Top-level rules


Acceptable objective expressions come in one of two forms:

  • Convex minimization: minimize( expr ), where expr is convex (or real affine).
  • Concave maximization: maximize( expr ), where expr is concave (or real affine).

A model does not need to have an objective; such a problem is called a feasibility problem. CVX will attempt to find a single point that satisfies all of the constraints.


Acceptable constraints come in one of four forms:

  • A less-than inequality constraint, using <=, where the left side is convex and the right side is concave.
  • A greater-than inequality constraint, using >=, where the left side is concave and the right side is convex.
  • An equality constraint, using ==, where both the left and right-hand sides are affine.
  • A set membership constraint, using <In>, involving affine expressions. (See Set membership for more details.)

Non-equality constraints, constructed using ~=, are never allowed. (Such constraints are not convex.)

CVX treats strict < > inequalities identically to non-strict <= >= inequalities, so to avoid confusion the use of strict inequalities is strongly discouraged. For more information, see Strict inequalities below.

Inequality constraints must be real. Equality constraints, on the other hand, may be complex. Complex equality constraints are equivalent to two real equality constraints, one for the real part and one for the imaginary part. An equality constraint with a real side and a complex side, then, has the effect of constraining the imaginary part of the complex side to be zero.

Expression rules

Each scalar expression and subexpression is analyzed to determin its curvature and sign. Vectors, matrices, and arrays are analyzed on an elementwise basis.


CVX considers four types of curvature: curvature: constant, affine, convex, and concave. The reader should already be familiar with these definitions. But for review, a function \(f:\mathbf{R}^n\rightarrow\mathbf{R}\) defined on all \(\mathbf{R}^n\), the categories have the following meanings:

\[\begin{split}\begin{array}{l@{\quad}ll} \text{constant} & f(\alpha x + (1-\alpha)y) = f(x) & \forall x,y\in\mathbf{R}^n,~\alpha\in\mathbf{R} \\ \text{affine} & f(\alpha x + (1-\alpha)y) = \alpha f(x) + (1-\alpha) f(y) & \forall x,y\in\mathbf{R}^n,~\alpha\in\mathbf{R} \\ \text{convex} & f(\alpha x + (1-\alpha)y) \leq \alpha f(x) + (1-\alpha) f(y) & \forall x,y\in\mathbf{R}^n,~\alpha\in[0,1] \\ \text{concave} & f(\alpha x + (1-\alpha)y) \geq \alpha f(x) + (1-\alpha) f(y) & \forall x,y\in\mathbf{R}^n,~\alpha\in[0,1] \end{array}\end{split}\]

There is, of course, significant overlap in these categories: constant expressions are also affine, and (real) affine expressions are both convex and concave. Convex and concave expressions are real by definition, but constants and affine expressions can be complex.

CVX does not determine convexity using the above definitions. Instead, curvature is determined recursively applying the following rules. While this list may seem long, it is for the most part an enumeration of basic rules of convex analysis for combining convex, concave, and affine forms: sums, multiplication by scalars, and so forth.

  • A valid constant expression is
    • any well-formed expression that immediately evaluates to a finite value.
  • A valid affine expression is
    • a valid constant expression;
    • a declared variable;
    • the sum or difference of affine expressions;
    • the product of an affine expression and a constant.
    • a valid affine function expression—see Composition rules;
  • A valid convex expression is
    • a valid constant or affine expression;
    • the sum of two or more convex expressions;
    • the difference between a convex expression and a concave expression;
    • the product of a convex expression and a nonnegative constant;
    • the product of a concave expression and a nonpositive constant;
    • the negation of a concave expression;
    • a valid convex function expression—see Composition rules;
    • an affine scalar raised to a constant power \(p\geq 1\), \(p\neq3,5,7,9,...\);
    • a convex scalar quadratic form—see Scalar quadratic forms.
  • A valid concave expression is
    • a valid constant or affine expression;
    • the sum of two or more concave expressions;
    • the difference between a concave expression and a convex expression;
    • the product of a concave expression and a nonnegative constant;
    • the product of a convex expression and a nonpositive constant;
    • the negation of a convex expression;
    • a valid concave function expression—see Composition rules;
    • a concave scalar raised to a power \(p\in(0,1)\);
    • a concave scalar quadratic form—see Scalar quadratic forms.

We note that the set of rules listed above is redundant; there are much smaller, equivalent sets of rules. For matrix and array expressions, these rules are applied on an elementwise basis.

Of particular note is that these expression rules generally forbid products between nonconstant expressions, with the exception of scalar quadratic forms. For example, the expression x*sqrt(x) happens to be a convex function of x, but its convexity cannot be verified using the CVX ruleset, and so is rejected. (It can be expressed as pow_p(x,3/2), however.) We call this the no-product rule:

  • The product or ratio of two non-constant (affine, convex, concave) expressions is forbidden.

Adherence to the no-product rule will go a long way to insuring that you construct valid expressions. There is one notable exception to this rule, however: see Scalar quadratic forms below. But quadratic forms are, strictly speaking, an unnecessary convenience, since CVX includes a quad_form function that provides the same functionality.


CVX also keeps track of the sign of an expression as well. Expressions are classified as positive, negative, and unknown sign. In a slight abuse of notation, nonnegative expressions are also treated as positive, and nonpositive expressions are also treated as negative. It should be noted that CVX does not perform any sort of advanced interval analysis to determine if an expression is positive or negative. As with curvature, it draws its conclusions by applying a simple set of rules:

  • A “positive” expression is
    • a positive constant (or zero);
    • a variable declared nonnegative (see Variables);
    • a diagonal element of a variable declared semidefinite (see Variables);
    • a call to any function specifically labeled as positive (see Functions below);
    • a negative expression multiplied by a negative constant;
    • a positive expression multiplied by a positive constant;
    • the sum of positive expressions.
  • A “negative” expression is
    • a negative constant (or zero);
    • a call to any function specifically labeled as negative (see Functions below);
    • a negative expression multiplied by a positive constant;
    • a positive expression multiplied by a negative constant;
    • the sum of negative expressions.

That’s it! Any expression whose sign cannot be determined from these rules is classified as having unknown sign. For example, the expression x - 1 has unknown sign—even if a constraint in the model ensures that x >= 1. These rules provide just enough information to CVX to give the user more flexibility in how it combines functions together; more on this in Sign-dependent monotonicity below.

Function expressions

Now let us consider how CVX classifies an expression of the form \(f(\arg_1,\arg_2,\dots,\arg_n)\), where \(f\) is a function from CVX’s function library, and each argument \(arg_k\) is an otherwise well- posed scalar CVX expression. In the case where a MATLAB function accepts vector, matrix, or array arguments, everything we discuss here is applied in an elementwise fashion. For instance, the expression norm(x), where x is a vector of length \(n\), can be thought of as a function expression involving \(n\) separate scalar arguments.

Function classification

In order to proceed, we must first understand the properties of the function \(f\) itself. As with basic expressions, CVX categorizes functions according to their curvature and sign. They also obtain two more attributes as well: monotonicity and domain. For functions with only one argument, the categorization is straightforward. Some examples are given in the table below.


The domain of a function is simply the set of points over which a function is well-defined. For a convex or concave function, this set is always convex. The domain serves as an implicit constraint on the function’s input. For instance, if we form sqrt(x+1) in a CVX specification, a new constraint x+1>=0 is automatically assumed. There is no need to add such a constraint separately. Monotonicity is also considered with respect to the function’s domain; so, for instance, sqrt(x) is considered increasing, since that is indeed the case for all nonnegative inputs.

CVX does not consider a function to be convex or concave if it is so only over a portion of its domain, even if the argument is constrained to lie in one of these portions. For example, consider the function \(1/x\). This function is convex for \(x>0\), and concave for \(x<0\). But you can never write 1/x in CVX (unless x is constant), even if you have imposed a constraint such as x>=1, which restricts x to lie in the convex portion of function. You can, however, use the CVX function inv_pos(x), listed above, which is defined to have the domain :math:mathbb{R}_{++}. CVX recognizes this function as convex and decreasing.


CVX considers two types of monotonicity: increasing and decreasing. In a slight abuse of notation, we classify nondecreasing functions as increasing, and nonincreasing functions as decreasing. These categories have the following meanings:

\[\begin{split}\begin{array}{l@{\quad}l} \text{increasing} & x \geq y ~~\Longrightarrow~~ f(x) \geq f(y) \\ \text{decreasing} & x \geq y ~~\Longrightarrow~~ f(x) \leq f(y) \end{array}\end{split}\]

A function that is neither increasing or decreasing is called nonmonotonic. In more recent versiojns of CVX, we also consider sign-dependent monotonicity. For example, the functions square(x) and abs(x) are decreasing for negative \(x\) and increasing for positive \(x\). Previous versions of CVX classifed these functions as nonmonotonic, which affected their use in compositions; more on this in Sign-dependent monotonicity below.

For functions with multiple arguments, curvature is always considered jointly, but monotonicity can be considered on an argument-by-argument basis. For example, the function quad_over_lin(x,y)

\[\begin{split}f_{\text{quad\_over\_lin}}(x,y) = \begin{cases} |x|^2/y & y > 0 \\ +\infty & y\leq 0 \end{cases}\end{split}\]

is jointly convex in both \(x\) and \(y\) and decreasing in \(y\), and exhibits sign-dependent monotonicity in x.

Some functions are convex, concave, or affine only for a subset of its arguments. For example, the function norm(x,p) where p \geq 1 is convex only in its first argument. Whenever this function is used in a CVX specification, then, the remaining arguments must be constant, or CVX will issue an error message. Such arguments correspond to a function’s parameters in mathematical terminology; e.g.,

\[f_p(x):\mathbf{R}^n\rightarrow\mathbf{R}, \quad f_p(x) \triangleq \|x\|_p\]

So it seems fitting that we should refer to such arguments as parameters in this context as well. Henceforth, whenever we speak of a CVX function as being convex, concave, or affine, we will assume that its parameters are known and have been given appropriate, constant values.

Composition rules

Armed with relevant information about \(f\) and the classification of the arguments \(\arg_k\) according to the rules in Expression rules, we may proceed to classify the full expression. We call the rules that govern these function expressions the composition rules.

Perhaps the most basic composition rule in convex anaysis is that convexity is closed under composition with an affine mapping. For example, function square(x)—which, as its name implies, computes \(f(x)=x^2\)—is convex for real arguments x. So if x is a real variable of dimension \(n\), a is a constant \(n\)-vector, and b is a constant, the expression

square( a' * x + b )

is accepted by CVX, which knows that it is convex.

The affine composition rule is just one one case in a more sophisiticated composition ruleset. Here is the complete set:

  • The function expression \(f(\arg_1,\arg_2,\dots,\arg_n)\) is affine if \(f\) is affine and every expression \(\arg_k\) is affine.
  • The function expression \(f(\arg_1,\arg_2,\dots,\arg_n)\) is convex if \(f\) is convex (or affine), and if one of the following is true for every expression \(\arg_k\):
    • \(\arg_k\) is affine.
    • \(\arg_k\) is convex, and the function is increasing in argument \(k\).
    • \(\arg_k\) is concave, and the function is decreasing in argument \(k\).
  • The function expression \(f(\arg_1,\arg_2,\dots,\arg_n)\) is concave if \(f\) is concave (or affine), and if one of the following is true for every expression \(\arg_k\):
    • \(\arg_k\) is affine.
    • \(\arg_k\) is concave, and the function is increasing in argument \(k\).
    • \(\arg_k\) is convex, and the function is decreasing in argument \(k\).

For more background on these composition rules, see Convex Optimization, Section 3.2.4.

Let us examine some examples. The maximum function is convex and increasing in every argument, so it can accept any convex expressions as arguments. For example, if x is a vector variable, then

max( abs( x ) )

obeys the “convex/increasing/convex” composition rule, and is therefore accepted by CVX, and classified as convex. As another example, consider the sum function, which is both convex and concave (since it is affine), and increasing in each argument. Therefore the expressions

sum( square( x ) )
sum( sqrt( x ) )

are recognized as valid in CVX, and classified as convex and concave, respectively. The first one follows from the “convex/increasing/convex” rule, while the second follows from the “concave/increasing/concave” rule.

Most people who know basic convex analysis like to think of these simpler examples in terms of more specific rules: a maximum of convex functions is convex, and a sum of convex (concave) functions is convex (concave). But as you can see, these rules are just special cases of the this general composition ruleset. In fact, with the exception of scalar quadratic expressions, the entire DCP ruleset can be thought of as special cases of these rules.

For a more complex example, suppose x is a vector variable, and A, b, and f are constants with appropriate dimensions. CVX recognizes the expression

sqrt(f'*x) + min(4,1.3-norm(A*x-b))

as concave. Consider the term sqrt(f'*x). CVX recognizes that sqrt is concave and f'*x is affine, so it concludes that sqrt(f'*x) is concave. Now consider the second term min(4,1.3-norm(A*x-b)). CVX recognizes that min is concave and increasing, so it can accept concave arguments. CVX recognizes that 1.3-norm(A*x-b) is concave, since it is the difference of a constant and a convex function. So CVX concludes that the second term is also concave. The whole expression is then recognized as concave, since it is the sum of two concave functions.

For a negative example, we can return to the original expression presented in the beginnnig of this chapter, sqrt( x^2 + 1 ). Assuming that x is a scalar variable, this is the composition of a concave, increasing function sqrt and a convex expression x^2+1. According to the composition rules, sqrt can accept a concave argument, not a convex argument, so CVX rejects it. On the other hand, norm([x 1]) is the composition of a convex function norm and an affine expression [x 1], so CVX can indeed accept that.

Sign-dependent monotonicity

Monotonicity is clearly a critical aspect of the rules for nonlinear compositions. Previous versions of CVX enforced these rules in a way that occasionally produced some unfortunate consequences. For example, consider the expression

square( square( x ) + 1 )

where x is a scalar variable. This expression is in fact convex, since \((x^2+1)^2 = x^4+2x^2+1\) is convex. However, previous versions of CVX used to reject this expression, because square is nonmontonic; and so it may not accept a convex argument according to the strictest reading of the composition rules above. Indeed, the square of a convex function is not, in general, convex: for example, \((x^2-1)^2 = x^4-2x^2+1\) is not convex.

In practice, this explanation may proved unsatisfying. After all, even though square is nonmonotonic over the entire real line, the expression square(x)+1 has a range of \([1,+\infty)\). And over that interval, square is increasing. Therefore, one could justifiably claim that the composition rules are satisfied it this case.

The latest versions of CVX implement a simple but effective approach for extending the composition rules to cover such cases: sign-dependent monotonicity. To accomplish this, functions that are positive or negative over their entire domain are noted as such, so this information can be used in the sign analysis described in Sign above. Furthermore, each functions monotonicity is considered with respect to the sign of its input. So, for example, square is increasing for positive inputs, and decreasing for negative inputs.

Under this new regime, we can now see how square(square(x)+1) can be accepted by CVX. First, CVX knows that square is nonnegative; and as the sum of two nonnegative terms, it draws the same conclusion about square(x)+1. Because of this, CVX can conclude that the outer instance to square is increasing. CVX determines that this expression is the composition of a convex, increasing function and a convex argument, and it is accepted by the ruleset.

Clearly, sign-dependent monotonicity, and the simple rule-based sign analysis performed in CVX, is limited. For example, entr( x ) defined above is increasing for \(x\geq 1/e\) and decreasing for \(x\leq 1/e\), but CVX does not consider that. But our experience with implementations found in CVXPY, the Stanford DCP expression analyzer, and our internal version of CVX suggest that this covers nearly all of the cases CVX users are likely to encounter.

Scalar quadratic forms

In its pure form, the DCP ruleset forbids even the use of simple quadratic expressions such as x * x (assuming x is a scalar variable). For practical reasons, we have chosen to make an exception to the ruleset to allow for the recognition of certain specific quadratic forms that map directly to certain convex quadratic functions (or their concave negatives) in the CVX atom library:

x .* x square( x ) (real x)
conj( x ) .* x square_abs( x )
y' * y sum_square_abs( y )
(A*x-b)'*Q*(Ax-b) quad_form( A*x - b, Q )

CVX detects the quadratic expressions such as those on the left above, and determines whether or not they are convex or concave; and if so, translates them to an equivalent function call, such as those on the right above.

CVX examines each single product of affine expressions, and each single squaring of an affine expression, checking for convexity; it will not check, for example, sums of products of affine expressions. For example, given scalar variables x and y, the expression

x ^ 2 + 2 * x * y + y ^2

will cause an error in CVX, because the second of the three terms 2 * x * y, is neither convex nor concave. But the equivalent expressions

( x + y ) ^ 2
( x + y ) * ( x + y )

will be accepted.

CVX actually completes the square when it comes across a scalar quadratic form, so the form need not be symmetric. For example, if z is a vector variable, a, b are constants, and Q is positive definite, then

( z + a )' * Q * ( z + b )

will be recognized as convex. Once a quadratic form has been verified by CVX, it can be freely used in any way that a normal convex or concave expression can be, as described in Expression rules.

Quadratic forms should actually be used less frequently in disciplined convex programming than in a more traditional mathematical programming framework, where a quadratic form is often a smooth substitute for a nonsmooth form that one truly wishes to use. In CVX, such substitutions are rarely necessary, because of its support for nonsmooth functions. For example, the constraint

sum( ( A * x - b ) .^ 2 ) <= 1

is equivalently represented using the Euclidean norm:

norm( A * x - b ) <= 1

With modern solvers, the second form is more naturally represented using a second-order cone constraint—so the second form may actually be more efficient. In fact, in our experience, the non-squared form will often be handled more accurately. So we strongly encourage you to re-evaluate the use of quadratic forms in your models, in light of the new capabilities afforded by disciplined convex programming.

Strict inequalities

As mentioned in Constraints, strict inequalities <, > are interpreted in an identical fashion to nonstrict inequalities >=, <=. It is important to note that CVX cannot guarantee that an inequality will be strictly satisfied at the solution it computes. This is not simply a choice we have made in CVX; it is a natural consequence of both the underlying mathematics and the design of convex optimization solvers. For that reason, we strongly discourage the use of strict inequalities in CVX, and a future version may remove them altogether.

When a strict inequality is essential to your model, you may need to take additional steps to ensure compliance. In some cases, this can be accomplished through normalization. For instance, consider a set of homogeneous equations and inequalities:

\[A x = 0, \quad C x \preceq 0, \quad x \succ 0\]

Except for the strict inequality, \(x=0\) would be an acceptable solution; indeed the need to avoid the origin is the very reason for the strict inequality. However, note that if a given \(x\) satisfies these constraints, then so does \(\alpha x\) for all \(\alpha>0\). By eliminating this degree of freedom with normalization, we can eliminate the strict inequality; for instance:

\[A x = 0, \quad C x \preceq 0, \quad x \succ 0, \quad \mathbf{1}^T x = 1\]

If normalization is not a valid approach for your model, you may simply need to convert the strict inequality into a non-strict one by adding a small offset; e.g., convert x > 0 to, say, x >= 1e-4. Note that the bound needs to be large enough so that the underlying solver considers it numerically significant.

Finally, note that for some functions like log(x) and inv_pos(x), which have domains defined by strict inequalities, the domain restriction is handled by the function itself. You do not need to add an explicit constraint x > 0 to your model to guarantee that the solution is positive.

Log convexity

Given our strong emphasis on adherence to the DCP ruleset, experienced users of CVX may be surprised to accidentally stumble upon certain expressions involving log and exp that violate the ruleset but are accepted anyway; for example, log(exp(x)+1). It turns out that this is an artifact of CVX’s support for geometric programming; and since it also requires the use of CVX’s experimental successive approximation approach, it is unsupported. Nevertheless, advanced users may be interested in reading more about these “hidden” rules in the Advanced topics chapter.