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Great news! More details soon.
 
Congrats to Continuum's own Michael Grant + the CVXR team for winning the Beale Orchard Hayes Prize for Excellence in Computational Mathematical Programming this week at ISMP 2015 for their TFOCS software! Not only is this award an incredible achievement, but this is actually the second time in a row Michael has received this award! Read more about the TFOCS project here: http://cvxr.com/

We are so excited to have such talented Software Developers on the Continuum Team. Congrats Michael!
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It really is a wonderful source of encouragement that CVX is trusted enough to be incorporated into teaching. You’ve made our day, Prof. Beck, thank you!
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A lot of people have asked for CVX on Octave over the years. Of course we want people to use CVX without the expense of a MATLAB license, but we just haven't been able to offer it yet---Octave itself needed changes. But it is coming very soon, and we have a live demo to prove it! Click the link for more details.
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Royi Avital's profile photoHao Chen's profile photoCVX Research's profile photoBehzad Samadi's profile photo
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Thank you!
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Professor Stephen Boyd and colleagues at Stanford University are offering a MOOC version of their well-known Convex Optimization course this winter. This course has been taught using Stanford's internal video distance learning platform for years, so it seems well suited for "porting" to the OpenEdX MOOC platform.

Our Matlab package CVX will be used extensively throughout. The Mathworks is providing time-limited Matlab licenses to all enrollees for the duration of the course. In addition, they are building a web interface to MATLAB so that you can build and programs and CVX models from any modern web browser.

If you haven't had a chance to learn about convex optimization more formally, here's a great opportunity.
About This Course. This course concentrates on recognizing and solving convex optimization problems that arise in applications. The syllabus includes: convex sets, functions, and optimization problems; basics of convex analysis; least-squares, linear and quadratic programs, ...
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We're very grateful that optimization experts like +Erling Andersen of +MOSEK ApS take the time to answer questions of CVX users on the CVX Forum (http://ask.cvxr.com). Here he offers a bit of a summary of a question that he and I both receive often: what is the complexity of the optimization algorithms used by CVX?
 
Busy day at the blog.  There is nothing new just a summary based on several answers I have given on the net.
First a clarification conic quadratic optimization and second order cone optimization is the same thing. I prefer the name conic quadratic optimization. Frequently it is asked on the internet what is the computational complex...
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Coursera course Linear and Integer Programming using CVX

A friend decided to take Coursera's new course in linear and integer programming, and alerted me that CVX is one of several tools they are recommending that their students use! Fantastic! One of the supplementary videos is devoted to introducing CVX, using the classic diet problem as an example, and gives users a tour of the web site as well as of the software itself.

I suspect that fans of CVX are probably already going to be familiar with the material in this course. Still, if you or anyone you know is interested, the course is still in its first week, so it's not too late to sign up!

EDITED TO ADD: The Mathworks is actually offering special time-limited licenses to MATLAB for course participants. So it looks like many folks who might not even have MATLAB will still have an opportunity to try CVX. (And sometime in the unspecified future, MATLAB will no longer be necessary... Stay tuned...)
Linear and Integer Programming is a free online class taught by Sriram Sankaranarayanan and Shalom D. Ruben of University of Colorado Boulder
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when will be the next course scheduled ?
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Do you
1) use Windows;
2) like GNU #Octave ;
3) know how to use Git;
4) enjoy trying out unsupported, bleeding-edge dev builds?

Then I invite you to try out the dev version of CVX 3.0 beta with Octave 4.0. To do so, you'll need to grab the Octave 4.0 binaries, and the "rework" branch of the CVX GitHub repo. 

Some important notes:

--- This is Windows only. I do not have binaries for Mac and Linux, and I cannot afford the time to build them myself. So I'm waiting for supported binaries for these platforms before I proceed. If you're a heavy Octave user you should consider contributing to the binary creation effort!

--- Complex variables will not work due to a known bug in Octave.

--- For some reason, there are issues with the GUI and plotting with Octave 4.0 on Windows 8.x. This is also a known bug.

--- Again, there is no support yet, and I mean that. Please don't clog up our support site or even the discussion forum with issues just yet. By all means, please feel free to report your experiences in the comments below. When I announce that the Octave support to be "beta worthy" I will invite bug reports.
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+Gorkem Ozkaya, that's great. My goal is to make it such that if you do recompile yourself, you don't have to move anything into the "o_win" subdirectory. I have to do that for precompiled binaries because Octave uses the same file suffix for all platforms. But if you compile your own, they might as well live right next to their MATLAB MEX counterparts. But I might not have the paths set up just right yet.
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After a lot of hard work by the GNU #Octave team, Octave 4.0.0 has been released. Well done folks! The good news for me is that the CVX-critical patches I submitted were included. The bad news for me is that this means I have some work to do... :-)
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As of February 20, over 10000 students are enrolled in CVX101, Stanford University’s online course in convex optimization offered by Professor Stephen Boyd and colleagues. Our Matlab package CVX is being used throughout the course. 

To provide enrollees with a gentle introduction to the software, Prof. Boyd recorded the following video. If you’re not yet a CVX user yourself, you might find it useful as well!
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CVX Research's profile photoXiang E. Xiang (Xex)'s profile photoSouad Tahraoui's profile photo
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I like it
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What's this? Someone actually reads CVX source code and can understand it? Fantastic! Oh, wait, he's a +MOSEK employee. Of course! :-)
 
cholinc(X, 'inf') was a blessing to all of us who wanted to make a quick MATLAB implementation of a primal-dual interior-point method.  It has been deprecated for some time, and in R2013a it was finally removed. Luckily, +Michael Grant  has shown us that it can be done as efficiently in just a few lines of code. The comments are mine, and I currently use tolLDL = eps or 2*eps, since the 4*eps suggested in cvx/functions/@cvx/quad_form.m resulted in very slow convergence on one of my test problems due to the slightly less accurate search direction.
---------
function R = cholinf( X )
  % Any symmetric matrix can be L*D*L' factorized into a
  % lower-triangular L and block-diagonal D with block-sizes
  % of 1x1 and 2x2 only. Fixing all 2x2 blocks, and negative
  % 1x1 blocks, of D to zero, we obtain the approximated
  % Cholesky factorization: (L*sqrt(D))*(L*sqrt(D))'.
  %
  [ R, DD, prm ] = ldl(X, 'upper', 'vector' );
  tt = diag(DD,1) == 0;
  tt = [ tt ; true ] & [ true ; tt ] & ( diag(DD) > tolLDL*sum(diag(X)) );
  DD = diag(DD);
  R  = bsxfun(@times, sqrt( DD(tt,:) ), R(tt,:) );
  if any( diff(prm) ~= 1 )
    R( :, prm ) = R;
  end
end
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We're grateful to the California Institute of Technology for agreeing to our proposal to release TFOCS under a permissive open source license. Effective immediately, TFOCS is governed by a 3-Clause BSD license, and its source code has been checked into Github. More details at the link.
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Hello CVX users! One of our users alerted us to a significant performance problem that is present in builds 982-1009 of CVX 2.0. (Type "cvx_version" to see your build number.) If you're using this version, I recommend an upgrade to 1010 or later. The problem is not present in version 1.2, so if you need to keep using that version, you're fine.
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Convex Optimization Modeling Software
Introduction
CVX Research, Inc. is a company formed by Michael C. Grant, Ph.D. and Professor Stephen Boyd of Stanford University specifically to support the continued development and support of CVX, a well-known Matlab-based modeling framework for convex optimization. We also host TFOCS, a framework for building customized first-order convex solvers in Matlab.
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