# The DCP ruleset¶

CVX enforces the conventions dictated by the disciplined convex programming ruleset, or DCP ruleset for short. CVX will issue an error message whenever it encounters a violation of any of the rules, so it is important to understand them before beginning to build models. The rules are drawn from basic principles of convex analysis, and are easy to learn, once you’ve had an exposure to convex analysis and convex optimization.

The DCP ruleset is a set of sufficient, but not necessary, conditions for convexity. So it is possible to construct expressions that violate the ruleset but are in fact convex. As an example consider the entropy function, $$-\sum_{i=1}^n x_i \log x_i$$, defined for $$x>0$$, which is concave. If it is expressed as

- sum( x .* log( x ) )


CVX will reject it, because its concavity does not follow from any of the composition rules. (Specifically, it violates the no-product rule described in Expression rules.) Problems involving entropy, however, can be solved, by explicitly using the entropy function,

sum( entr( x ) )


which is in the base CVX library, and thus recognized as concave by CVX. If a convex (or concave) function is not recognized as convex or concave by CVX, it can be added as a new atom; see Adding new functions to the atom library.

As another example consider the function $$\sqrt{x^2+1}=\|[x~1]\|_2$$, which is convex. If it is written as

norm( [ x 1 ] )


(assuming x is a scalar variable or affine expression) it will be recognized by CVX as a convex expression, and therefore can be used in (appropriate) constraints and objectives. But if it is written as

sqrt( x^2 + 1 )


CVX will reject it, since convexity of this function does not follow from the CVX ruleset.

## A taxonomy of curvature¶

In disciplined convex programming, a scalar expression is classified by its curvature. There are four categories of curvature: constant, affine, convex, and concave. For a function $$f:\mathbf{R}^n\rightarrow\mathbf{R}$$ defined on all $$\mathbf{R}^n$$, the categories have the following meanings:

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

Of course, there is significant overlap in these categories. For example, constant expressions are also affine, and (real) affine expressions are both convex and concave.

Convex and concave expressions are real by definition. Complex constant and affine expressions can be constructed, but their usage is more limited; for example, they cannot appear as the left- or right-hand side of an inequality constraint.

## Top-level rules¶

CVX supports three different types of disciplined convex programs:

• A minimization problem, consisting of a convex objective function and zero or more constraints.
• A maximization problem, consisting of a concave objective function and zero or more constraints.
• A feasibility problem, consisting of one or more constraints and no objective.

## Constraints¶

Three types of constraints may be specified in disciplined convex programs:

• An equality constraint, constructed using ==, where both sides are affine.
• 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.

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

One or both sides of an equality constraint may be complex; inequality constraints, on the other hand, must be real. A complex equality constraint is 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 has the effect of constraining the imaginary part of the complex side to be zero.

As discussed in Set membership, CVX enforces set membership constraints (e.g., $$x\in S$$) using equality constraints. The rule that both sides of an equality constraint must be affine applies to set membership constraints as well. In fact, the returned value of set atoms like semidefinite() and lorentz() is affine, so it is sufficient to simply verify the remaining portion of the set membership constraint. For composite values like { x, y }, each element must be affine.

### 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.

## Expression rules¶

So far, the rules as stated are not particularly restrictive, in that all convex programs (disciplined or otherwise) typically adhere to them. What distinguishes disciplined convex programming from more general convex programming are the rules governing the construction of the expressions used in objective functions and constraints.

Disciplined convex programming determines the curvature of scalar expressions by 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 Matlab expression that evaluates to a finite value.
• A valid affine expression is
• a valid constant expression;
• a declared variable;
• a valid call to a function in the atom library with an affine result;
• the sum or difference of affine expressions;
• the product of an affine expression and a constant.
• A valid convex expression is
• a valid constant or affine expression;
• a valid call to a function in the atom library with a convex result;
• an affine scalar raised to a constant power $$p\geq 1$$, $$p\neq3,5,7,9,...$$;
• 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 concave expression is
• a valid constant or affine expression;
• a valid call to a function in the atom library with a concave result;
• a concave scalar raised to a power $$p\in(0,1)$$;
• 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.

If an expression cannot be categorized by this ruleset, it is rejected by CVX. For matrix and array expressions, these rules are applied on an elementwise basis. We note that the set of rules listed above is redundant; there are much smaller, equivalent sets of rules.

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, and paying close attention to it will go a long way to insuring that the expressions you construct are valid.

## Functions¶

In CVX, functions are categorized in two attributes: curvature (constant, affine, convex, or concave) and monotonicity (nondecreasing, nonincreasing, or nonmonotonic). Curvature determines the conditions under which they can appear in expressions according to the expression rules given above. Monotonicity determines how they can be used in function compositions, as we shall see in the next section.

For functions with only one argument, the categorization is straightforward. Some examples are given in the table below.

Function Meaning Curvature Monotonicity
sum( x ) $$\sum_i x_i$$ affine nondecreasing
abs( x ) $$|x|$$ convex nonmonotonic
log( x ) $$\log x$$ concave nondecreasing
sqrt( x ) $$\sqrt x$$ concave nondecreasing

Following standard practice in convex analysis, convex functions are interpreted as $$+\infty$$ when the argument is outside the domain of the function, and concave functions are interpreted as $$-\infty$$ when the argument is outside its domain. In other words, convex and concave functions in CVX are interpreted as their extended-valued extensions.

This has the effect of automatically constraining the argument of a function to be in the function’s domain. For example, if we form sqrt(x+1) in a CVX specification, where x is a variable, then x will automatically be constrained to be larger than or equal to $$-1$$. There is no need to add a separate constraint, x>=-1, to enforce this.

Monotonicity of a function is determined in the extended sense, i.e., including the values of the argument outside its domain. For example, sqrt(x) is determined to be nondecreasing since its value is constant ($$-\infty$$) for negative values of its argument; then jumps up to $$0$$ for argument zero, and increases for positive values of its argument.

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. As an 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 $$1/x$$. You can use the CVX function inv_pos(x), defined as $$1/x$$ for $$x>0$$ and $$\infty$$ otherwise, for the convex portion of $$1/x$$; CVX recognizes this function as convex and nonincreasing. In CVX, you can express the concave portion of $$1/x$$, where $$x$$ is negative, using -inv_pos(-x), which will be correctly recognized as concave and nonincreasing.

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 $$y$$, but it is monotonic (nonincreasing) only in $$y$$.

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.

## Compositions¶

A basic rule of convex analysis is that convexity is closed under composition with an affine mapping. This is part of the DCP ruleset as well:

• A convex, concave, or affine function may accept an affine expression (of compatible size) as an argument. The result is convex, concave, or affine, respectively.

For example, consider the function square(x), which is provided in the CVX atom library. This function squares its argument; i.e., it computes x.*x. (For array arguments, it squares each element independently.) It is in the CVX atom library, and known to be convex, provided its argument is real. 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 above is a special case of a more sophisticated composition rule, which we describe now. We consider a function, of known curvature and monotonicity, that accepts multiple arguments. For convex functions, the rules are:

• If the function is nondecreasing in an argument, that argument must be convex.
• If the function is nonincreasing in an argument, that argument must be concave.
• If the function is neither nondecreasing or nonincreasing in an argument, that argument must be affine.

If each argument of the function satisfies these rules, then the expression is accepted by CVX, and is classified as convex. Recall that a constant or affine expression is both convex and concave, so any argument can be affine, including as a special case, constant.

The corresponding rules for a concave function are as follows:

• If the function is nondecreasing in an argument, that argument must be concave.
• If the function is nonincreasing in an argument, that argument must be convex.
• If the function is neither nondecreasing or nonincreasing in an argument, that argument must be affine.

In this case, the expression is accepted by CVX, and classified as concave.

For more background on these composition rules, see Convex Optimization, Section 3.2.4. In fact, with the exception of scalar quadratic expressions, the entire DCP ruleset can be thought of as special cases of these six rules.

Let us examine some examples. The maximum function is convex and nondecreasing 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 first of the six composition rules 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 nondecreasing 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 first rule for convex functions; and the second one follows from the first rule for concave functions.

Most people who know basic convex analysis like to think of these examples in terms of the more specific rules: a maximum of convex functions is convex, and a sum of convex (concave) functions is convex (concave). But these rules are just special cases of the general composition rules above. Some other well known basic rules that follow from the general composition rules are:

• a nonnegative multiple of a convex (concave) function is convex (concave);
• a nonpositive multiple of a convex (concave) function is concave (convex).

Now we consider a more complex example in depth. 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 nondecreasing, 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.

The composition rules are sufficient but not necessary for the classification to be correct, so some expressions which are in fact convex or concave will fail to satisfy them, and so will be rejected by CVX. For example, if x is a vector variable, the expression

sqrt( sum( square( x ) ) )


is rejected by CVX, because there is no rule governing the composition of a concave nondecreasing function with a convex function. Of course, the workaround is simple in this case: use norm( x ) instead, since norm is in the atom library and known by CVX to be convex.

## Monotonicity in nonlinear compositions¶

Monotonicity is a critical aspect of the rules for nonlinear compositions. This has some consequences that are not so obvious, as we shall demonstrate here by 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. But CVX will reject the expression, because the outer square cannot accept a convex argument. 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.

There are several ways to modify the expression above to comply with the ruleset. One way is to write it as x^4 + 2*x^2 + 1, which CVX recognizes as convex, since CVX allows positive even integer powers using the ^ operator. (Note that the same technique, applied to the function $$(x^2-1)^2$$, will fail, since its second term is concave.)

Another approach is to use the alternate outer function square_pos, included in the CVX library, which represents the function $$(x_+)^2$$, where $$x_+ = \max\{0,x\}$$. Obviously, square and square_pos coincide when their arguments are nonnegative. But square_pos is nondecreasing, so it can accept a convex argument. Thus, the expression

square_pos( square( x ) + 1 )


is mathematically equivalent to the rejected version above (since the argument to the outer function is always positive), but it satisfies the DCP ruleset and is therefore accepted by CVX.

This is the reason several functions in the CVX atom library come in two forms: the “natural” form, and one that is modified in such a way that it is monotonic, and can therefore be used in compositions. Other such “monotonic extensions” include sum_square_pos and quad_pos_over_lin. If you are implementing a new function yourself, you might wish to consider if a monotonic extension of that function would also be useful.

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.

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