### Abstract

Original language | English |
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Place of Publication | Ithaca |

Publisher | Cornell University Library |

Number of pages | 23 |

Volume | 1411.6867 |

Publication status | Published - 1 Dec 2014 |

### Publication series

Name | Preprint at arXiv |
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Volume | 1411.6867v3 |

### Fingerprint

### Keywords

- polynomial optimization
- semidefinite optimization
- Lasserre hierarchy

### Cite this

*Convergence Analysis for Lasserre's Measure-based Hierarchy of Upper Bounds for Polynomial Optimization*. (Preprint at arXiv; Vol. 1411.6867v3). Ithaca: Cornell University Library.

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*Convergence Analysis for Lasserre's Measure-based Hierarchy of Upper Bounds for Polynomial Optimization*. Preprint at arXiv, vol. 1411.6867v3, vol. 1411.6867, Cornell University Library, Ithaca.

**Convergence Analysis for Lasserre's Measure-based Hierarchy of Upper Bounds for Polynomial Optimization.** / de Klerk, E.; Laurent, M.; Sun, Z.

Research output: Book/Report › Report › Professional

TY - BOOK

T1 - Convergence Analysis for Lasserre's Measure-based Hierarchy of Upper Bounds for Polynomial Optimization

AU - de Klerk, E.

AU - Laurent, M.

AU - Sun, Z.

PY - 2014/12/1

Y1 - 2014/12/1

N2 - We consider the problem of minimizing a continuous function f over a compact set K. We analyze a hierarchy of upper bounds proposed by Lasserre in [SIAM J. Optim. 21(3) (2011), pp. 864--885], obtained by searching for an optimal probability density function h on K which is a sum of squares of polynomials, so that the expectation ∫ K f(x)h(x)dx is minimized. We show that the rate of convergence is O(1/r √ ) , where 2r is the degree bound on the density function. This analysis applies to the case when f is Lipschitz continuous and K is a full-dimensional compact set satisfying some boundary condition (which is satisfied, e.g., for polytopes and the Euclidean ball). The r-th upper bound in the hierarchy may be computed using semidefinite programming if f is a polynomial of degree d, and if all moments of order up to 2r+d of the Lebesgue measure on K are known, which holds for example if K is a simplex, hypercube, or a Euclidean ball.

AB - We consider the problem of minimizing a continuous function f over a compact set K. We analyze a hierarchy of upper bounds proposed by Lasserre in [SIAM J. Optim. 21(3) (2011), pp. 864--885], obtained by searching for an optimal probability density function h on K which is a sum of squares of polynomials, so that the expectation ∫ K f(x)h(x)dx is minimized. We show that the rate of convergence is O(1/r √ ) , where 2r is the degree bound on the density function. This analysis applies to the case when f is Lipschitz continuous and K is a full-dimensional compact set satisfying some boundary condition (which is satisfied, e.g., for polytopes and the Euclidean ball). The r-th upper bound in the hierarchy may be computed using semidefinite programming if f is a polynomial of degree d, and if all moments of order up to 2r+d of the Lebesgue measure on K are known, which holds for example if K is a simplex, hypercube, or a Euclidean ball.

KW - polynomial optimization

KW - semidefinite optimization

KW - Lasserre hierarchy

M3 - Report

VL - 1411.6867

T3 - Preprint at arXiv

BT - Convergence Analysis for Lasserre's Measure-based Hierarchy of Upper Bounds for Polynomial Optimization

PB - Cornell University Library

CY - Ithaca

ER -