Nonconvex Minimization Problems

نویسنده

  • IVAR EKELAND
چکیده

I. The central result. The grandfather of it all is the celebrated 1961 theorem of Bishop and Phelps (see [7], [8]) that the set of continuous linear functionals on a Banach space E which attain their maximum on a prescribed closed convex bounded subset X c E is norm-dense in £*. The crux of the proof lies in introducing a certain convex cone in E, associating with it a partial ordering, and applying to the latter a transfinite induction argument (Zorn's lemma). This argument was later used in different settings by Brondsted and Rockafellar (see [9]) and by F. Browder (see [11]). The various situations can be adequately summarized in a diagram:

برای دانلود رایگان 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 the Global Convergence of Majorization Minimization Algorithms for Nonconvex Optimization Problems

In this paper, we study the global convergence of majorization minimization (MM) algorithms for solving nonconvex regularized optimization problems. MM algorithms have received great attention in machine learning. However, when applied to nonconvex optimization problems, the convergence of MM algorithms is a challenging issue. We introduce theory of the KurdykaLojasiewicz inequality to address ...

متن کامل

Nonconvex Sorted l1 Minimization for Sparse Approximation

The l1 norm is the tight convex relaxation for the l0 “norm” and has been successfully applied for recovering sparse signals. However, for problems with fewer samples than required for accurate l1 recovery, one needs to apply nonconvex penalties such as lp “norm”. As one method for solving lp minimization problems, iteratively reweighted l1 minimization updates the weight for each component bas...

متن کامل

Advances in Canonical Duality Theory with Applications to Global Optimization

Canonical duality theory is a potentially powerful methodology, which can be used to solve a wide class of discrete and continuous global optimization problems. This paper presents a brief review and recent developments of this theory with applications to some well-know problems including polynomial minimization, mixed integer and fractional programming, nonconvex minimization with nonconvex qu...

متن کامل

On the Global Convergence of the PERRY-SHANNO Method for Nonconvex Unconstrained Optimization Problems

In this paper, we prove the global convergence of the Perry-Shanno’s memoryless quasi-Newton (PSMQN) method with a new inexact line search when applied to nonconvex unconstrained minimization problems. Preliminary numerical results show that the PSMQN with the particularly line search conditions are very promising.

متن کامل

Unified global optimality conditions for smooth minimization problems with mixed variables

In this paper we establish necessary as well as sufficient conditions for a given feasible point to be a global minimizer of smooth minimization problems with mixed variables. These problems, for instance, cover box constrained smooth minimization problems and bivalent optimization problems. In particular, our results provide necessary global optimality conditions for difference convex minimiza...

متن کامل

Composite Optimization by Nonconvex Majorization-Minimization

Many tasks in imaging can be modeled via the minimization of a nonconvex composite function. A popular class of algorithms for solving such problems are majorizationminimization techniques which iteratively approximate the composite nonconvex function by a majorizing function that is easy to minimize. Most techniques, e.g. gradient descent, utilize convex majorizers in order guarantee that the ...

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

عنوان ژورنال:

دوره   شماره 

صفحات  -

تاریخ انتشار 2007