Concave Minimization Via Collapsing Polytopes

نویسندگان

  • James E. Falk
  • Karla L. Hoffman
چکیده

JSTOR is a not-for-profit service that helps scholars, researchers, and students discover, use, and build upon a wide range of content in a trusted digital archive. We use information technology and tools to increase productivity and facilitate new forms of scholarship. For more information about JSTOR, please contact [email protected]. INFORMS is collaborating with JSTOR to digitize, preserve and extend access to Operations Research.

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

Frank-Wolfe Algorithms for Saddle Point Problems

We extend the Frank-Wolfe (FW) optimization algorithm to solve constrained smooth convex-concave saddle point (SP) problems. Remarkably, the method only requires access to linear minimization oracles. Leveraging recent advances in FW optimization, we provide the first proof of convergence of a FW-type saddle point solver over polytopes, thereby partially answering a 30 year-old conjecture. We a...

متن کامل

On the Minimization of Concave Information Functionals for Unsupervised Classification via Decision Trees

A popular method for unsupervised classification of high-dimensional data via decision trees is characterized as minimizing the empirical estimate of a concave information functional. It is shown that minimization of such functionals under the true distributions leads to perfect classification.

متن کامل

Machine Learning via Polyhedral Concave Minimization

Two fundamental problems of machine learning misclassi cation minimization and feature selection are formulated as the minimization of a concave function on a polyhedral set Other formulations of these problems utilize linear programs with equilibrium constraints which are generally intractable In contrast for the proposed concave minimization formulation a successive linearization algorithm wi...

متن کامل

Restricted isometry property of matrices with independent columns and neighborly polytopes by random sampling

This paper considers compressed sensing matrices and neighborliness of a centrally symmetric convex polytope generated by vectors ±X1, . . . ,±XN ∈ Rn, (N ≥ n). We introduce a class of random sampling matrices and show that they satisfy a restricted isometry property (RIP) with overwhelming probability. In particular, we prove that matrices with i.i.d. centered and variance 1 entries that satis...

متن کامل

Feature Selection via Concave Minimization and Support Vector Machines

Computational comparison is made between two feature selection approaches for nding a separating plane that discriminates between two point sets in an n-dimensional feature space that utilizes as few of the n features (dimensions) as possible. In the concave minimization approach [19, 5] a separating plane is generated by minimizing a weighted sum of distances of misclassi ed points to two para...

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:
  • Operations Research

دوره 34  شماره 

صفحات  -

تاریخ انتشار 1986