# Convex set, Operations research, Mathematics, Optimization, Convex function, Calculus

On Feb 1, 2021
RT @BachFrancis: Least squares are great! They are even greater owing to self-concordance. See why in this month blog post: https://t.co/xcmZMVs0rB https://t.co/JYhlsIU6wp
Open

### Going beyond least-squares – I : self-concordant analysis of Newton method

Classical examples are all linear and quadratic functions, the negative logarithm, the negative log-determinant, or the negative logarithm of quadratic functions. Classical analysis of ...

### Le Thi Hoai An

Website of Le Thi Hoai An

### Effortless optimization through gradient flows

From gradient descent to gradient flows Gradient descent is the most classical iterative algorithm to minimize differentiable functions. I chose two starting points famous to cyclists, Col ...

### Demystifying the Math of Support Vector Machines (SVM)

This article was written by Krishna Kumar Mahto.  So, three days into SVM, I was 40% frustrated, 30% restless, 20% irritated and 100% inefficient in terms of g…

### Pushing the boundaries of convex optimization

Convex optimization has many applications ranging from operations research and machine learning to quantum information theory.

### Incremental and Parallel Machine Learning Algorithms With Automated Learning Rate Adjustments

The existing machine learning algorithms for minimizing the convex function over a closed convex set suffer from slow convergence because their learning rates must be determined before ...

### Machine Learning Glossary

Compilation of key machine-learning and TensorFlow terms, with beginner-friendly definitions.

$Adaptive Algorithms: L* bounds and AdaGrad$

In this lecture, we will explore a bit more under which conditions we can get better regret upper bounds than $latex {O(D L \sqrt{T})}&fg=000000$. Also, we will obtain this improved ...

### Nevergrad: An open source tool for derivative-free optimization

We are open-sourcing Nevergrad, a Python3 library that makes it easier to perform gradient-free optimizations used in many machine learning tasks.

### Essential Math for Data Science: ‘Why’ and ‘How’

Data summaries and descriptive statistics, central tendency, variance, covariance, correlation, Basic probability: basic idea, expectation, probability calculus, Bayes theorem, conditional ...

### Essential Math for Data Science

Almost all the techniques of modern data science, including machine learning, have a deep mathematical underpinning. A solid understanding of a few key topics will give you an edge in the ...

### Convex geometry of quantum resource quantification

We present an explicit application of the results to the resource theories of multi-level coherence, entanglement of Schmidt number k, multipartite entanglement, as well as magic states, ...