# Disciplined Geometric Programming¶

Disciplined geometric programming (DGP) is an analog of DCP for log-log convex functions, that is, functions of positive variables that are convex with respect to the geometric mean instead of the arithemetic mean.

While DCP is a ruleset for constructing convex programs, DGP is a ruleset for log-log convex programs (LLCPs), which are problems that are convex after the variables, objective functions, and constraint functions are replaced with their logs, an operation that we refer to as a log-log transformation. Every geometric program (GP) and generalized geometric program (GGP) is an LLCP, but there are LLCPs that are neither GPs nor GGPs.

CVXPY lets you form and solve DGP problems, just as it does for DCP problems. For example, the following code solves a simple geometric program,

import cvxpy as cp

# DGP requires Variables to be declared positive via pos=True.
x = cp.Variable(pos=True)
y = cp.Variable(pos=True)
z = cp.Variable(pos=True)

objective_fn = x * y * z
constraints = [
4 * x * y * z + 2 * x * z <= 10, x <= 2*y, y <= 2*x, z >= 1]
problem = cp.Problem(cp.Maximize(objective_fn), constraints)
problem.solve(gp=True)
print("Optimal value: ", problem.value)
print("x: ", x.value)
print("y: ", y.value)
print("z: ", z.value)


and it prints the below output.

Optimal value: 1.9999999938309496
x: 0.9999999989682057
y: 1.999999974180587
z: 1.0000000108569758


Note that to solve DGP problems, you must pass the option gp=True to the solve() method.

This section explains what DGP is, and it shows how to construct and solve DGP problems using CVXPY. At the end of the section are tables listing all the atoms that can be used in DGP problems, similar to the tables presented in the section on DCP atoms.

For an in-depth reference on DGP, see our accompanying paper. For interactive code examples, check out our notebooks.

Note: DGP is a recent addition to CVXPY. If you have feedback, please file an issue or make a pull request on Github.

## Log-log curvature¶

Just as every Expression in CVXPY has a curvature (constant, affine, convex, concave, or unknown), every Expression also has a log-log curvature.

A function $$f : D \subseteq \mathbf{R}^n_{++} \to \mathbf{R}$$ is said to be log-log convex if the function $$F(u) = \log f(e^u)$$, with domain $$\{u \in \mathbf{R}^n : e^u \in D\}$$, is convex (where $$\mathbf{R}^n_{++}$$ denotes the set of positive reals and the logarithm and exponential are meant elementwise); the function $$F$$ is called the log-log transformation of f. The function $$f$$ is log-log concave if $$F$$ is concave, and it is log-log affine if $$F$$ is affine.

Every log-log affine function has the form

$f(x) = cx_1^{a_1}x_2^{a_2} \ldots x_n^{a_n}$

where $$x$$ is in $$\mathbf{R}^n_{++}$$, the $$a_i$$ are real numbers, and $$c$$ is a positive scalar. In the context of geometric programming, such a function is called a monomial function. A sum of monomials, known as a posynomial function in geometric programming, is a log-log convex function; A table of all the atoms with known log-log curvature is presented at the end of this page.

In the below table, $$F$$ is the log-log transformation of $$f$$, $$u=\log x$$, and $$v=\log y$$, where $$x$$ and $$y$$ are in the domain of $$f$$

Log-Log Curvature Meaning
log-log constant $$F$$ is a constant (so f is a positive constant)
log-log affine $$F(\theta u + (1-\theta)v) = \theta F(u) + (1-\theta)F(v), \; \forall u, \; v,\; \theta \in [0,1]$$
log-log convex $$F(\theta u + (1-\theta)v) \leq \theta F(u) + (1-\theta)F(v), \; \forall u, \; v,\; \theta \in [0,1]$$
log-log concave $$F(\theta u + (1-\theta)v) \geq \theta F(u) + (1-\theta)F(v), \; \forall u, \; v,\; \theta \in [0,1]$$
unknown DGP analysis cannot determine the curvature

CVXPY’s log-log curvature analysis can flag Expressions as unknown even when they are log-log convex or log-log concave. Note that any log-log constant expression is also log-log affine, and any log-log affine expression is log-log convex and log-log concave.

The log-log curvature of an Expression is stored in its .log_log_curvature attribute. For example, running the following script

import cvxpy as cp

x = cp.Variable(pos=True)
y = cp.Variable(pos=True)

constant = cp.Constant(2.0)
monomial = constant * x * y
posynomial = monomial + (x ** 1.5) * (y ** -1)
reciprocal = posynomial ** -1
unknown = reciprocal + posynomial

print(constant.log_log_curvature)
print(monomial.log_log_curvature)
print(posynomial.log_log_curvature)
print(reciprocal.log_log_curvature)
print(unknown.log_log_curvature)


prints the following output.

LOG-LOG CONSTANT
LOG-LOG AFFINE
LOG-LOG CONVEX
LOG-LOG CONCAVE
UNKNOWN


You can also check the log-log curvature of an Expression by calling the methods is_log_log_constant(), is_log_log_affine(), is_log_log_convex(), is_log_log_concave(). For example, posynomial.is_log_log_convex() would evaluate to True.

## Log-log curvature rules¶

For an Expression to have known log-log curvature, all of the Constants, Variables, and Parameters it refers to must be elementwise positive. A Constant is positive if its numerical value is positive. Variables and Parameters are positive only if the keyword argument pos=True is supplied to their constructors (e.g., x = cvxpy.Variable(shape=(), pos=True)). To summarize, when formulating a DGP problem, all Constants should be elementwise positive, and all Variables and Parameters must be constructed with the attribute pos=True.

DGP analysis is exactly analogous to DCP analysis. It is based on a library of atoms (functions) with known monotonicity and log-log curvature and a a single composition rule. The library of atoms is presented at the end of this page; the composition rule is stated below.

A function $$f(expr_1, expr_2, ..., expr_n)$$ is log-log convex if $$\text{ } f$$ is a log-log convex function and for each $$expr_{i}$$ one of the following conditions holds:

• $$f$$ is increasing in argument $$i$$ and $$expr_{i}$$ is log-log convex.
• $$f$$ is decreasing in argument $$i$$ and $$expr_{i}$$ is log-log concave.
• $$expr_{i}$$ is log-log affine.

A function $$f(expr_1, expr_2, ..., expr_n)$$ is log-log concave if $$\text{ } f$$ is a log-log concave function and for each $$expr_{i}$$ one of the following conditions holds:

• $$f$$ is increasing in argument $$i$$ and $$expr_{i}$$ is log-log concave.
• $$f$$ is decreasing in argument $$i$$ and $$expr_{i}$$ is log-log convex.
• $$expr_{i}$$ is log-log affine.

A function $$f(expr_1, expr_2, ..., expr_n)$$ is log-log affine if $$\text{ } f$$ is an log-log affine function and each $$expr_{i}$$ is log-log affine.

If none of the three rules apply, the expression $$f(expr_1, expr_2, ..., expr_n)$$ is marked as having unknown curvature.

If an Expression satisfies the composition rule, we colloquially say that the Expression “is DGP.” You can check whether an Expression is DGP by calling the method is_dgp(). For example, the assertions in the following code block will pass.

import cvxpy as cp

x = cp.Variable(pos=True)
y = cp.Variable(pos=True)

monomial = 2.0 * constant * x * y
posynomial = monomial + (x ** 1.5) * (y ** -1)

assert monomial.is_dgp()
assert posynomial.is_dgp()


An Expression is DGP precisely when it has known log-log curvature, which means at least one of the methods is_log_log_constant(), is_log_log_affine(), is_log_log_convex(), is_log_log_concave() will return True.

## DGP problems¶

A Problem is constructed from an objective and a list of constraints. If a problem follows the DGP rules, it is guaranteed to be an LLCP and solvable by CVXPY. The DGP rules require that the problem objective have one of two forms:

• Minimize(log-log convex)
• Maximize(log-log concave)

The only valid constraints under the DGP rules are

• log-log affine == log-log affine
• log-log convex <= log-log concave
• log-log concave >= log-log convex

You can check that a problem, constraint, or objective satisfies the DGP rules by calling object.is_dgp(). Here are some examples of DGP and non-DGP problems:

import cvxpy as cp

# DGP requires Variables to be declared positive via pos=True.
x = cp.Variable(pos=True)
y = cp.Variable(pos=True)
z = cp.Variable(pos=True)

objective_fn = x * y * z
constraints = [
4 * x * y * z + 2 * x * z <= 10, x <= 2*y, y <= 2*x, z >= 1]
assert objective_fn.is_log_log_concave()
assert all(constraint.is_dgp() for constraint in constraints)
problem = cp.Problem(cp.Maximize(objective_fn), constraints)
assert problem.is_dgp()

# All Variables must be declared as positive for an Expression to be DGP.
w = cp.Variable()
objective_fn = w * x * y
assert not objective_fn.is_dgp()
problem = cp.Problem(cp.Maximize(objective_fn), constraints)
assert not problem.is_dgp()


CVXPY will raise an exception if you call problem.solve(gp=True) on a non-DGP problem.

## DGP atoms¶

This section of the tutorial describes the DGP atom library, that is, the atomic functions with known log-log curvature and monotonicity. CVXPY uses the function information in this section and the DGP rules to mark expressions with a log-log curvature. Note that every DGP expression is positive.

### Infix operators¶

The infix operators +, *, / are treated as atoms. The operators * and / are log-log affine functions. The operator + is log-log convex in both its arguments.

Note that in CVXPY, expr1 * expr2 denotes matrix multiplication when expr1 and expr2 are matrices; if you’re running Python 3, you can alternatively use the @ operator for matrix multiplication. Regardless of your Python version, you can also use the matmul atom to multiply two matrices. To multiply two arrays or matrices elementwise, use the multiply atom. Finally, to take the product of the entries of an Expression, use the prod atom.

### Transpose¶

The transpose of any expression can be obtained using the syntax expr.T. Transpose is a log-log affine function.

### Power¶

For any CVXPY expression expr, the power operator expr**p is equivalent to the function power(expr, p). Taking powers is a log-log affine function.

### Scalar functions¶

A scalar function takes one or more scalars, vectors, or matrices as arguments and returns a scalar. Note that several of these atoms may be applied along an axis; see the API reference or the DCP atoms tutorial for more information.

Function Meaning Domain Log-log curvature  Monotonicity

geo_mean(x)

geo_mean(x, p)

$$p \in \mathbf{R}^n_{+}$$

$$p \neq 0$$

$$x_1^{1/n} \cdots x_n^{1/n}$$

$$\left(x_1^{p_1} \cdots x_n^{p_n}\right)^{\frac{1}{\mathbf{1}^T p}}$$

$$x \in \mathbf{R}^n_{+}$$ log-log affine incr.
harmonic_mean(x) $$\frac{n}{\frac{1}{x_1} + \cdots + \frac{1}{x_n}}$$ $$x \in \mathbf{R}^n_{+}$$ log-log concave incr.
max(X) $$\max_{ij}\left\{ X_{ij}\right\}$$ $$X \in\mathbf{R}^{m \times n}_{++}$$ log-log convex incr.
min(X) $$\min_{ij}\left\{ X_{ij}\right\}$$ $$X \in\mathbf{R}^{m \times n}_{++}$$ log-log concave incr.

norm(x)

norm(x, 2)

$$\sqrt{\sum_{i} \lvert x_{i} \rvert^2 }$$ $$X \in\mathbf{R}^{n}_{++}$$ log-log convex incr.
norm(X, “fro”) $$\sqrt{\sum_{ij}X_{ij}^2 }$$ $$X \in\mathbf{R}^{m \times n}_{++}$$ log-log convex incr.
norm(X, 1) $$\sum_{ij}\lvert X_{ij} \rvert$$ $$X \in\mathbf{R}^{m \times n}_{++}$$ log-log convex incr.
norm(X, “inf”) $$\max_{ij} \{\lvert X_{ij} \rvert\}$$ $$X \in\mathbf{R}^{m \times n}_{++}$$ log-log convex incr.

pnorm(X, p)

$$p \geq 1$$

or p = 'inf'

$$\|X\|_p = \left(\sum_{ij} |X_{ij}|^p \right)^{1/p}$$ $$X \in \mathbf{R}^{m \times n}_{++}$$ log-log convex incr.

pnorm(X, p)

$$0 < p < 1$$

$$\|X\|_p = \left(\sum_{ij} X_{ij}^p \right)^{1/p}$$ $$X \in \mathbf{R}^{m \times n}_{++}$$ log-log convex incr.
prod(X) $$\prod_{ij}X_{ij}$$ $$X \in\mathbf{R}^{m \times n}_{++}$$ log-log affine incr.
quad_form(x, P) $$x^T P x$$ $$x \in \mathbf{R}^n$$, $$P \in \mathbf{R}^{n \times n}_{++}$$ log-log convex incr.
quad_over_lin(X, y) $$\left(\sum_{ij}X_{ij}^2\right)/y$$

$$x \in \mathbf{R}^n_{++}$$

$$y > 0$$

log-log convex

in $$X_{ij}$$

decr. in $$y$$

sum(X) $$\sum_{ij}X_{ij}$$ $$X \in\mathbf{R}^{m \times n}_{++}$$ log-log convex incr.
sum_squares(X) $$\sum_{ij}X_{ij}^2$$ $$X \in\mathbf{R}^{m \times n}_{++}$$ log-log convex incr.
trace(X) $$\mathrm{tr}\left(X \right)$$ $$X \in\mathbf{R}^{n \times n}_{++}$$ log-log convex incr.
pf_eigenvalue(X) spectral radius of $$X$$ $$X \in\mathbf{R}^{n \times n}_{++}$$ log-log convex incr.

### Elementwise functions¶

These functions operate on each element of their arguments. For example, if X is a 5 by 4 matrix variable, then sqrt(X) is a 5 by 4 matrix expression. sqrt(X)[1, 2] is equivalent to sqrt(X[1, 2]).

Elementwise functions that take multiple arguments, such as maximum and multiply, operate on the corresponding elements of each argument. For example, if X and Y are both 3 by 3 matrix variables, then maximum(X, Y) is a 3 by 3 matrix expression. maximum(X, Y)[2, 0] is equivalent to maximum(X[2, 0], Y[2, 0]). This means all arguments must have the same dimensions or be scalars, which are promoted.

Function Meaning Domain Curvature  Monotonicity
diff_pos(x, y) $$x - y$$ $$0 < y < x$$ log-log concave

incr. in $$x$$

decr. in $$y$$

entr(x) $$-x \log (x)$$ $$0 < x < 1$$ log-log concave None
exp(x) $$e^x$$ $$x > 0$$ log-log convex incr.
log(x) $$\log(x)$$ $$x > 1$$ log-log concave incr.
maximum(x, y) $$\max \left\{x, y\right\}$$ $$x,y > 0$$ log-log convex incr.
minimum(x, y) $$\min \left\{x, y\right\}$$ $$x, y > 0$$ log-log concave incr.
multiply(x, y) $$x*y$$ $$x, y > 0$$ log-log affine incr.
one_minus_pos(x) $$1 - x$$ $$0 < x < 1$$ log-log concave decr.
power(x, 0) $$1$$ $$x > 0$$ constant constant
power(x, p) $$x$$ $$x > 0$$ log-log affine

for $$p > 0$$

for $$p < 0$$

sqrt(x) $$\sqrt x$$ $$x > 0$$ log-log affine incr.
square(x) $$x^2$$ $$x > 0$$ log-log affine incr.

### Vector/matrix functions¶

A vector/matrix function takes one or more scalars, vectors, or matrices as arguments and returns a vector or matrix.

Function Meaning Domain Curvature  Monotonicity
bmat([[X11,…,X1q], …, [Xp1,…,Xpq]]) $$\left[\begin{matrix} X^{(1,1)} & \cdots & X^{(1,q)} \\ \vdots & & \vdots \\ X^{(p,1)} & \cdots & X^{(p,q)} \end{matrix}\right]$$ $$X^{(i,j)} \in\mathbf{R}^{m_i \times n_j}_{++}$$ log-log affine incr.
diag(x) $$\left[\begin{matrix}x_1 & & \\& \ddots & \\& & x_n\end{matrix}\right]$$ $$x \in\mathbf{R}^{n}_{++}$$ log-log affine incr.
diag(X) $$\left[\begin{matrix}X_{11} \\\vdots \\X_{nn}\end{matrix}\right]$$ $$X \in\mathbf{R}^{n \times n}_{++}$$ log-log affine incr.
eye_minus_inv(X) $$(I - X)^{-1}$$ $$X \in\mathbf{R}^{n \times n}_{++}, \lambda_{\text{pf}}(X) < 1$$ log-log convex incr.
hstack([X1, …, Xk]) $$\left[\begin{matrix}X^{(1)} \cdots X^{(k)}\end{matrix}\right]$$ $$X^{(i)} \in\mathbf{R}^{m \times n_i}_{++}$$ log-log affine incr.
matmul(X, Y) $$XY$$ $$X \in\mathbf{R}^{m \times n}_{++}, Y \in\mathbf{R}^{n \times p}_{++}`$$ log-log convex incr.
resolvent(X) $$(sI - X)^{-1}$$ $$X \in\mathbf{R}^{n \times n}_{++}, \lambda_{\text{pf}}(X) < s$$ log-log convex incr.
reshape(X, (n’, m’)) $$X' \in\mathbf{R}^{m' \times n'}$$

$$X \in\mathbf{R}^{m \times n}_{++}$$

$$m'n' = mn$$

log-log affine incr.
vec(X) $$x' \in\mathbf{R}^{mn}$$ $$X \in\mathbf{R}^{m \times n}_{++}$$ log-log affine incr.
vstack([X1, …, Xk]) $$\left[\begin{matrix}X^{(1)} \\ \vdots \\X^{(k)}\end{matrix}\right]$$ $$X^{(i)} \in\mathbf{R}^{m_i \times n}_{++}$$ log-log affine incr.