# Examples¶

The examples shown here appear in the 2019 article by Murray, Chandrasekaran, and Wierman, titled Signomial and Polynomial Optimization via Relative Entropy and Partial Dualization. That paper introduced conditional SAGE certificates.

## Optimization Examples¶

### Example 1 (signomial)¶

We want to solve a constraint signomial program in three variables.

\begin{split}\begin{align*} \min_{x \in \mathbb{R}^3} &~ f(x) \doteq 0.5 \exp(x_1 - x_2) -\exp x_1 - 5 \exp(-x_2) \\ \text{s.t.} &~ g_1(x) \doteq 100 - \exp(x_2 - x_3) -\exp x_2 - 0.05 \exp(x_1 + x_3) \geq 0\\ &~ g_{2:4}(x) \doteq \exp(x) - (70,\,1,\, 0.5) \geq (0, 0, 0) \\ &~ g_{5:7}(x) \doteq (150,\,30,\,21) - \exp(x) \geq (0, 0, 0) \end{align*}\end{split}

It’s often easier to specify a signomial program by defining symbols y, which are related to variables x by y = exp(x). You can get a hold of these symbols y by using the function standard_sig_monomials, and providing a dimension of your desired variable.

import sageopt as so
y = so.standard_sig_monomials(3)
f = 0.5 * y * y ** -1 - y - 5 * y ** -1
gts = [100 -  y * y ** -1 - y - 0.05 * y * y,
y - 70,
y - 1,
y - 0.5,
150 - y,
30 - y,
21 - y]
eqs = []


Next we will pass our problem through a function called infer_domain. This function parses the given constraint signomials, and infers any which can be written in a tractable convex form with respect to the optimization variable x .

X = so.infer_domain(f, gts, eqs)


For this problem, it just so happens that all constraints can be written in a convex form. Taking this as given, we use the function sig_relaxation for the resulting problem. The dual formulation is used because we want to recover a solution.

dual = so.sig_relaxation(f, X=X, form='dual')
dual.solve(verbose=False)
solutions = so.sig_solrec(dual)
best_soln = solutions
print(best_soln)


Now let’s see if this solution is any good!

print("The recovered solution has objective value ...")
print('\t' + str(f(best_soln)))  # about -147.66666
print("The recovered solution has constraint violation ...")
constraint_levels = min([g(best_soln) for g in gts])  # zero!
violation = 0 if constraint_levels >= 0 else -constraint_levels
print('\t' + str(violation))
print('The level 0 SAGE bound is ... ')
print('\t' + str(dual.value))  # about -147.857


We can certify that the solution is actually much closer to optimality than the SAGE bound would suggest. We can easily construct and solve a level ell=3 SAGE relaxation to produce a stronger lower bound on this minimization problem.

dual = so.sig_relaxation(f, X=X, form='dual', ell=3)
dual.solve(verbose=False)
print('The level 3 SAGE bound is ... ')
print('\t' + str(dual.value))  # about  -147.6666


### Example 2 (signomial)¶

We want to solve the following equality-constrained signomial program.

\begin{split}\begin{align*} \min_{\substack{A \in \mathbb{R}^3_{++} \\ P \in \mathbb{R}_{++} }} &~ 10^4 (A_1 + A_2 + A_3) \\ \text{s.t.} &~ 10^4 + 0.01 A_1^{-1}A_3^{} - 7.0711 A_1^{-1} \geq 0 \\ &~ 10^4 + 0.00854 A_1^{-1}P - 0.60385(A_1^{-1} + A_2^{-1}) \geq 0 \\ &~ 70.7107 A_1^{-1} - A_1^{-1}P - A_{3}^{-1}P = 0 \\ &~ 10^4 \geq 10^4 A_1 \geq 10^{-4} \qquad 10^4 \geq 10^4 A_2 \geq 7.0711 \\ &~ 10^4 \geq 10^4 A_3 \geq 10^{-4} \qquad 10^4 \geq 10^4 P_{~} \geq 10^{-4} \end{align*}\end{split}

It is straightforward to compute a tight bound on the problem’s optimal objective, however solution recovery is difficult. Thus we show this problem in two forms: once with the equality constraint, and once where the inequality constraint is used to define a value of $$P$$ (which we can then substitute into the rest of the formulation). First we show the case with the equality constraint.

import sageopt as so
x = so.standard_sig_monomials(4)
A = x[:3]
P = x
f = 1e4 * sum(A)
main_gts = [
1e4 + 1e-2 * A / A - 7.0711 / A,
1e4  + 8.54e-3 * P/ A - 6.0385e-1 * (1.0 / A + 1.0 / A)
]
bounds = [
1e4 - 1e4 * A, 1e4 * A - 1e-4,
1e4 - 1e4 * A, 1e4 * A - 7.0711,
1e4 - 1e4 * A, 1e4 * A - 1e-4,
1e4 - 1e4 * P, 1e4 * P - 1e-4
]
gts = main_gts + bounds
eqs = [70.7107 / A + P / A - P / A]
X = so.infer_domain(f, bounds, [])
prim = so.sig_constrained_relaxation(f, main_gts, eqs, form='primal', X=X)
dual = so.sig_constrained_relaxation(f, main_gts, eqs, form='dual', X=X)
prim.solve(verbose=False)
dual.solve(verbose=False)
print('\n')
print(prim.value)
print(dual.value)


The equality constraint in this problem creates an unnecessary challenge in solution recovery. Since we usually want to recover optimal solutions, we reformulate the problem by substituting $$P \leftarrow 70.7107 A_3 / (A_1 + A_3)$$ from constraints which involved $$P$$.

A = so.standard_sig_monomials(3)
f = 1e4 * sum(A)
main_gts = [
1e4 + 1e-2 * A / A - 7.0711 / A,
1e4 * (A + A) + 8.54e-3 * (70.7012 * A * (A + A)) / A
- 6.0385e-1 * (A + A) * (1.0 / A + 1.0 / A)
]
bounds = [
1e4 - 1e4 * A, 1e4 * A - 1e-4,
1e4 - 1e4 * A, 1e4 * A - 7.0711,
1e4 - 1e4 * A, 1e4 * A - 1e-4,
A - 69.7107 * A, (1e8 * 70.7107 - 1) - A / A
]
gts = main_gts + bounds
X = so.infer_domain(f, gts, [])
dual = so.sig_constrained_relaxation(f, main_gts, [], form='dual', X=X)
dual.solve()
print('\n')
print(dual.value)
solns = so.sig_solrec(dual)
print(f(solns))


### Example 3 (polynomial)¶

In this example, we minimize

$f(x) = -64 \sum_{i=1}^7 \prod_{j \neq i} x_j$

over $$x \in [-1/2, 1/2]^7$$. We also want to recover optimal solutions.

import numpy as np
import sageopt as so
x = so.standard_poly_monomials(7)
f = 0
for i in range(7):
sel = np.ones(7, dtype=bool)
sel[i] = False
f -= 64 * np.prod(x[sel])
# ^ use simple NumPy functions to construct Polynomials!
gts = [0.25 - x[i]**2 for i in range(7)]  # -.5 <= x[i] <= .5
X = so.infer_domain(f, gts, [])
dual = so.poly_constrained_relaxation(f, [], [], form='dual', X=X)
dual.solve(verbose=False, solver='MOSEK')
print()
solns = so.poly_solrec(dual)
for sol in solns:
print(sol)


You can also try this example with ECOS. When using ECOS, you might want to use local solver refinement, as accessed in sageopt.local_refinement.

### Example 4 (polynomial)¶

We want to solve a degree six polynomial optimization problem in six variables.

\begin{split}\begin{align*} \min_{x \in \mathbb{R}^6} &~ f(x) \doteq x_1^6 - x_2^6 + x_3^6 - x_4^6 + x_5^6 - x_6^6 + x_1 - x_2 \\ \text{s.t.} &~ g_1(x) \doteq 2 x_{1}^{6}+3 x_{2}^{2}+2 x_{1} x_{2}+2 x_{3}^{6}+3 x_{4}^{2}+2 x_{3} x_{4}+2 x_{5}^{6}+3 x_{6}^{2}+2 x_{5} x_{6} \geq 0 \\ &~ g_2(x) \doteq 2 x_{1}^{2}+5 x_{2}^{2}+3 x_{1} x_{2}+2 x_{3}^{2}+5 x_{4}^{2}+3 x_{3} x_{4}+2 x_{5}^{2}+5 x_{6}^{2}+3 x_{5} x_{6} \geq 0 \\ &~ g_3(x) \doteq 3 x_{1}^{2}+2 x_{2}^{2}-4 x_{1} x_{2}+3 x_{3}^{2}+2 x_{4}^{2}-4 x_{3} x_{4}+3 x_{5}^{2}+2 x_{6}^{2}-4 x_{5} x_{6} \geq 0 \\ &~ g_4(x) \doteq x_{1}^{2}+6 x_{2}^{2}-4 x_{1} x_{2}+x_{3}^{2}+6 x_{4}^{2}-4 x_{3} x_{4}+x_{5}^{2}+6 x_{6}^{2}-4 x_{5} x_{6} \geq 0 \\ &~ g_5(x) \doteq x_{1}^{2}+4 x_{2}^{6}-3 x_{1} x_{2}+x_{3}^{2}+4 x_{4}^{6}-3 x_{3} x_{4}+x_{5}^{2}+4 x_{6}^{6}-3 x_{5} x_{6} \geq 0 \\ &~ g_{6:10}(x) \doteq 1 - g_{1:5}(x) \geq (0, 0, 0, 0, 0) \\ &~ g_{11:16}(x) = x \geq (0, 0, 0, 0, 0, 0) \end{align*}\end{split}

The sageopt approach to this problem is to write it first as a signomial program, and then perform solution recovery with consideration to the underlying polynomial structure. The solution recovery starts with sig_solrec as normal, but then we refine the solution with a special function local_refine_polys_from_sigs.

import sageopt as so

x = so.standard_sig_monomials(6)
f = x**6 - x**6 + x**6 - x**6 + x**6 - x**6 + x - x

expr1 = 2*x**6 + 3*x**2 + 2*x*x + 2*x**6 + 3*x**2 + 2*x*x + 2*x**6 + 3*x**2 + 2*x*x
expr2 = 2*x**2 + 5*x**2 + 3*x*x + 2*x**2 + 5*x**2 + 3*x*x + 2*x**2 + 5*x**2 + 3*x*x
expr3 = 3*x**2 + 2*x**2 - 4*x*x + 3*x**2 + 2*x**2 - 4*x*x + 3*x**2 + 2*x**2 - 4*x*x

expr4 = x**2 + 6*x**2 - 4*x*x + x**2 + 6*x**2 - 4*x*x + x**2 + 6*x**2 - 4*x*x
expr5 = x**2 + 6*x**2 - 4*x*x + x**2 + 6*x**2 - 4*x*x + x**2 + 6*x**2 - 4*x*x

gts = [expr3, expr4, expr5, 1 - expr1, 1 - expr2, 1 - expr3, 1 - expr4, 1 - expr5]
eqs = []

dual = so.sig_constrained_relaxation(f, gts, eqs, form='dual', p=1, q=1, ell=0)
dual.solve(verbose=False, solver='MOSEK')  # ECOS fails
y0 = so.sig_solrec(dual)
x_star = so.local_refine_polys_from_sigs(f, gts, eqs, y0)

print()
print(dual.value)
f_poly = f.as_polynomial()
print(f_poly(x_star))
print(x_star)


## Nonnegativity Examples¶

Although sageopt is designed around optimization, the mechanism by which sageopt operates is to certify nonnegativity by decomposing a given function into a “Sum of AGE-functions”. These AGE functions are nonnegative, and can be proven nonnegative in a relatively simple way. If you want to check nonnegativity of the AGE functions yourself (you might find yourself in this situation if a numerical solver seemed to struggle with a SAGE relaxation), then you can do that. Here we show how to get a hold on these AGE functions, from a given SAGE relaxation.

### Example 1 (signomial)¶

Consider the following optimization problem:

\begin{split}\begin{align*} \min_{x \in \mathbb{R}^3} &~ f(x) \doteq 0.5 \exp(x_1 - x_2) -\exp x_1 - 5 \exp(-x_2) \\ \text{s.t.} &~ g_1(x) \doteq 100 - \exp(x_2 - x_3) -\exp x_2 - 0.05 \exp(x_1 + x_3) \geq 0\\ &~ g_{2:4}(x) \doteq \exp(x) - (70,\,1,\, 0.5) \geq (0, 0, 0) \\ &~ g_{5:7}(x) \doteq (150,\,30,\,21) - \exp(x) \geq (0, 0, 0) \end{align*}\end{split}

We can produce a bound on this minimum with a primal SAGE relaxation.

import sageopt as so
y = so.standard_sig_monomials(3)
f = 0.5 * y * y ** -1 - y - 5 * y ** -1
gts = [100 -  y * y ** -1 - y - 0.05 * y * y,
y - 70, y - 1, y - 0.5,
150 - y, 30 - y, 21 - y]
X = so.infer_domain(f, gts, [])
prim = so.sig_relaxation(f, form='primal', ell=0, X=X)
prim.solve(solver='ECOS')


As long as the solver (here, ECOS) succeeds in solving the problem, the function f - prim.value should be nonnegative over the set represented by X. The intended proof that f - prim.value is nonnegative comes from the AGE functions participating in its decomposition. We can recover those functions as follows

sage_constraint = prim.user_cons  # a PrimalSageCone object
alpha = sagecon.alpha
agefunctions = []
for ci in sagecon.age_vectors.values():
s = so.Signomial(alpha, ci.value)
agefunctions.append(s)


You should find that one of these AGE functions has very small positive coefficients, and a large negative term. We can investigate this suspicious AGE function further. Specifically, we can transform the suspicious AGE function into a convex function, and then solve a constrained convex optimization problem using a function from scipy.

suspicious_age = agefunctions
convexified_suspicious_age = y * suspicious_age
import numpy as np
from scipy.optimize import fmin_cobyla
def sample_initial_point():
y1 = 70 + 80 * np.random.rand()
y2 = 1 + 29 * np.random.rand()
y3 = 0.5 + 20.5 * np.random.rand()
x0 = np.log([y1, y2, y3])
return x0
fmin_cobyla(convexified_suspicious_age,
sample_initial_point(), gts,
disp=1, maxfun=1e5, rhoend=1e-7)


You should find that no matter how many initial conditions you provide to scipy’s solver, the reported optimal objective is nonnegative.