$f,g_1,...,g_m$ be elements of the polynomial ring $\mathbb{R}[x_1,...,x_n]$. The paper deals with the general problem of computing a lower bound for $f$ on the subset of $\mathbb{R}^n$ defined by the inequalities $g_i\ge 0$, $i=1,...,m$. The paper shows that there is an algorithm for computing such a lower bound, based on geometric programming, which applies in a large number of cases. The algorithm extends and generalizes earlier algorithms of Ghasemi and Marshall, dealing with the case $m=0$, and of Ghasemi, Lasserre and Marshall, dealing with the case $m=1$ and $g_1= M-(x_1^d+\cdots+x_n^d)$. Here, $d$ is required to be an even integer $d \ge \max\{2,\deg(f)\}$. The algorithm is implemented in a SAGE program developed by the first author. The bound obtained is typically not as good as the bound obtained using semidefinite programming, but it has the advantage that it is computable rapidly, even in cases where the bound obtained by semidefinite programming is not computable.
[1]
Mehdi Ghasemi,et al.
Lower bounds for a polynomial in terms of its coefficients
,
2010
.
[2]
Jean B. Lasserre,et al.
Lower bounds on the global minimum of a polynomial
,
2014,
Comput. Optim. Appl..
[3]
Carla Fidalgo,et al.
Positive semidefinite diagonal minus tail forms are sums of squares
,
2011
.
[4]
Stephen P. Boyd,et al.
A tutorial on geometric programming
,
2007,
Optimization and Engineering.
[5]
Mehdi Ghasemi,et al.
Lower Bounds for Polynomials Using Geometric Programming
,
2012,
SIAM J. Optim..
[6]
Jean B. Lasserre,et al.
Global Optimization with Polynomials and the Problem of Moments
,
2000,
SIAM J. Optim..
[7]
Jean B. Lasserre.
Sufficient conditions for a real polynomial to be a sum of squares
,
2006
.