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2. List of PDF software - Wikipedia

   en.wikipedia.org/wiki/List_of_PDF_software

   deskUNPDF: PDF converter to convert PDFs to Word (.doc, docx), Excel (.xls), (.csv), (.txt), more; GSview: File:Convert menu item converts any sequence of PDF pages to a sequence of images in many formats from bit to tiffpack with resolutions from 72 to 204 × 98 (open source software) Google Chrome: convert HTML to PDF using Print > Save as PDF.

3. Convex optimization - Wikipedia

   en.wikipedia.org/wiki/Convex_optimization

   A convex optimization problem is defined by two ingredients: [5] [6] The objective function, which is a real-valued convex function of n variables, :;; The feasible set, which is a convex subset.

4. Convex function - Wikipedia

   en.wikipedia.org/wiki/Convex_function

   A function (in black) is convex if and only if the region above its graph (in green) is a convex set. A graph of the bivariate convex function x 2 + xy + y 2. Convex vs. Not convex

5. Convex analysis - Wikipedia

   en.wikipedia.org/wiki/Convex_analysis

   Convex analysis includes not only the study of convex subsets of Euclidean spaces but also the study of convex functions on abstract spaces. Convex analysis is the branch of mathematics devoted to the study of properties of convex functions and convex sets, often with applications in convex minimization, a subdomain of optimization theory.

6. Universal Document Converter - Wikipedia

   en.wikipedia.org/wiki/Universal_Document_Converter

   Universal Document Converter is a virtual printer and PDF creator for Microsoft Windows developed by fCoder Group. It can create PDF documents (as raster images or searchable text) and files in graphic formats JPEG, TIFF, PNG, GIF, PCX, DCX and BMP. [3] It can create graphic or PDF files from any document that can be printed.

7. Conic optimization - Wikipedia

   en.wikipedia.org/wiki/Conic_optimization

   Conic optimization is a subfield of convex optimization that studies problems consisting of minimizing a convex function over the intersection of an affine subspace and a convex cone. The class of conic optimization problems includes some of the most well known classes of convex optimization problems, namely linear and semidefinite programming.