Category Archives: Optimization

Theory of Convex Optimization for Machine Learning

I am extremely happy to release the first draft of my monograph based on the lecture notes published last year on this blog. (Comments on the draft are welcome!) The abstract reads as follows: This monograph presents the main mathematical … Continue reading

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Nesterov’s Accelerated Gradient Descent for Smooth and Strongly Convex Optimization

    About a year ago I described Nesterov’s Accelerated Gradient Descent in the context of smooth optimization. As I mentioned previously this has been by far the most popular post on this blog. Today I have decided to revisit this post to give a … Continue reading

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Guest post by Dan Garber and Elad Hazan: The Conditional Gradient Method, A Linear Convergent Algorithm – Part II/II

The goal of this post is to develop a Conditional Gradient method that converges exponentially fast while basically solving only a linear minimization problem over the domain on each iteration. To this end we consider the following relaxation of the … Continue reading

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Guest post by Dan Garber and Elad Hazan: The Conditional Gradient Method, A Linear Convergent Algorithm – Part I/II

In a previous post Sebastien presented and analysed the conditional gradient method for minimizing a smooth convex function over a compact and convex domain . The update step of the method is as follows,     where , . The … Continue reading

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The aftermath of ORF523 and the final

It has been two weeks since my last post on the blog, and I have to admit that it felt really good to take a break from my 2-posts-per-week regime of the previous months. Now that ORF523 is over the … Continue reading

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ORF523: Optimization with bandit feedback

In this last lecture we consider the case where one can only access the function with a noisy -order oracle (see this lecture for a definition). This assumption models the ‘physical reality’ of many practical problems (on the contrary to … Continue reading

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ORF523: Acceleration by randomization for a sum of smooth and strongly convex functions

In this lecture we are interested in the following problem:     where each is -smooth and strongly-convex. We denote by the condition number of these functions (which is also the condition number of the average). Using a gradient descent … Continue reading

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ORF523: Noisy oracles

Today we start the third (and last) part of our class titled: ‘Optimization and Randomness’. An important setting that we will explore is the one of optimization with noisy oracles: Noisy -order oracle: given it outputs such that . Noisy … Continue reading

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ORF523: Mirror Prox

Today I’m going to talk about a variant of Mirror Descent invented by Nemirovski in 2004 (see this paper). This new algorithm is called Mirror Prox and it is designed to minimize a smooth convex function over a compact convex … Continue reading

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ORF523: Mirror Descent, part II/II

We start with some of the standard setups for the Mirror Descent algorithm we described in the previous post. Standard setups for Mirror Descent ‘Ball’ setup. The simplest version of Mirror Descent is obtained by taking on The function is … Continue reading

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