Improving SDP bounds for minimizing quadratic functions over the l-ball

نویسندگان

  • Immanuel M. Bomze
  • Florian Frommlet
  • Martin Rubey
چکیده

In this note, we establish superiority of the so-called copositive bound over a bound suggested by Nesterov for the quadratic problem to minimize a quadratic form over the l-ball. We illustrate the improvement by simulation results. The copositive bound has the additional advantage that it can be easily extended to the inhomogeneous case of quadratic objectives including a linear term. We also indicate some improvements of the eigenvalue bound for the quadratic optimization over the l-ball with 1 < p < 2, at least for p close to one. This version: April 13, 2008

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

On semidefinite bounds for maximization of a non-convex quadratic objective over the l1 unit ball

We consider the non-convex quadratic maximization problem subject to the `1 unit ball constraint. The nature of the l1 norm structure makes this problem extremely hard to analyze, and as a consequence, the same difficulties are encountered when trying to build suitable approximations for this problem by some tractable convex counterpart formulations. We explore some properties of this problem, ...

متن کامل

A Semidefinite Programming Method for Integer Convex Quadratic Minimization

We consider the NP-hard problem of minimizing a convex quadratic function over the integer lattice Z. We present a semidefinite programming (SDP) method for obtaining a nontrivial lower bound on the optimal value of the problem. By interpreting the solution to the SDP relaxation probabilistically, we obtain a randomized algorithm for finding good suboptimal solutions. The effectiveness of the m...

متن کامل

Quadratic Programs with Hollows

Let F be a quadratically constrained, possibly nonconvex, bounded set, and let E1, . . . , El denote ellipsoids contained in F with non-intersecting interiors. We prove that minimizing an arbitrary quadratic q(·) over G := F\∪k=1 int(Ek) is no more difficult than minimizing q(·) over F in the following sense: if a given semidefinite-programming (SDP) relaxation for min{q(x) : x ∈ F} is tight, t...

متن کامل

An Explicit Convergence Rate for Nesterov's Method from SDP

The framework of Integral Quadratic Constraints (IQC) introduced by Lessard et al. (2014) reduces the computation of upper bounds on the convergence rate of several optimization algorithms to semi-definite programming (SDP). In particular, this technique was applied to Nesterov’s accelerated method (NAM). For quadratic functions, this SDP was explicitly solved leading to a new bound on the conv...

متن کامل

Solving the Single Machine Problem with Quadratic Earliness and Tardiness Penalties

  Nowadays, scheduling problems have a considerable application in production and service systems. In this paper, we consider the scheduling of n jobs on a single machine assuming no machine idleness, non-preemptive jobs and equal process times. In many of previous researches, because of the delivery dalays and holding costs, earliness and tardiness penalties emerge in the form of linear combin...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

عنوان ژورنال:

دوره   شماره 

صفحات  -

تاریخ انتشار 2005