TY - JOUR
T1 - Exact relaxations of non-convex variational problems
AU - Meziat, René
AU - Patiño, Diego
PY - 2008/8
Y1 - 2008/8
N2 - Here, we solve non-convex, variational problems given in the form Equation is presented where u ε(W 1,∞(0, 1)) k and f : ℝk → ℝ is a non-convex, coercive polynomial. To solve (1) we analyse the convex hull of the integrand at the point a, so that we can find vectors a1,...,aN ε ℝk and positive values λ1, . . . , λ N satisfying the non-linear equation (1, a, fc(a)) = ∑i=1M Nλ1(1, ai, f(ai)).(2) Thus, we can calculate minimizers of (1) by following a proposal of Dacorogna in (Direct Methods in the Calculus of Variations. Springer, Heidelberg, 1989). Indeed, we can solve (2) by using a semidefinite program based on multidimensional moments.
AB - Here, we solve non-convex, variational problems given in the form Equation is presented where u ε(W 1,∞(0, 1)) k and f : ℝk → ℝ is a non-convex, coercive polynomial. To solve (1) we analyse the convex hull of the integrand at the point a, so that we can find vectors a1,...,aN ε ℝk and positive values λ1, . . . , λ N satisfying the non-linear equation (1, a, fc(a)) = ∑i=1M Nλ1(1, ai, f(ai)).(2) Thus, we can calculate minimizers of (1) by following a proposal of Dacorogna in (Direct Methods in the Calculus of Variations. Springer, Heidelberg, 1989). Indeed, we can solve (2) by using a semidefinite program based on multidimensional moments.
KW - Calculus of variations
KW - Convex analysis
KW - Multidimensional moment problem
KW - Semidefinite programming
UR - http://www.scopus.com/inward/record.url?scp=45949108027&partnerID=8YFLogxK
U2 - 10.1007/s11590-008-0077-6
DO - 10.1007/s11590-008-0077-6
M3 - Article
AN - SCOPUS:45949108027
SN - 1862-4472
VL - 2
SP - 505
EP - 519
JO - Optimization Letters
JF - Optimization Letters
IS - 4
ER -