mathematics

Peter Richtarik

Peter Richtarik is a mathematics topic covered in the lgStudy science library. This page brings together a partial reference excerpt, illustrations, worked examples, real-world applications and a short study plan, so you can understand Peter Richtarik rather than just read about it. In short: Peter Richtarik is a Slovak mathematician and computer scientist working in the area of big data optimization and machine learning, known for his work on randomized coordinate descent algorithms, stochastic gradient descent and federated learning. He is currently a Professor of Computer Science at the King Abdullah University of Science and Technology.

Key takeaways

Peter Richtarik belongs to mathematics; place it in that map before memorising details.
Learn the definition first, then one example that makes the definition concrete.
Connect Peter Richtarik to a quantity you can measure, compute or draw — that is where exam questions come from.
Reproduce the core statement of Peter Richtarik from memory before moving on to harder problems.

Reference excerpt

Peter Richtarik is a Slovak mathematician and computer scientist working in the area of big data optimization and machine learning, known for his work on randomized coordinate descent algorithms, stochastic gradient descent and federated learning. He is currently a Professor of Computer Science at the King Abdullah University of Science and Technology.

Education Richtarik earned a master's degree in mathematics from Comenius University, Slovakia, in 2001, graduating summa cum laude. In 2007, he obtained a PhD in operations research from Cornell University, advised by Michael Jeremy Todd.

Career Between 2007 and 2009, he was a postdoctoral scholar in the Center for Operations Research and Econometrics and Department of Mathematical Engineering at Universite catholique de Louvain, Belgium, working with Yurii Nesterov. Between 2009 and 2019, Richtarik was a Lecturer and later Reader in the School of Mathematics at the University of Edinburgh. He is a Turing Fellow. Richtarik founded and organizes a conference series entitled "Optimization and Big Data".

Academic work Richtarik's early research concerned gradient-type methods, optimization in relative scale, sparse principal component analysis and algorithms for optimal design. Since his appointment at Edinburgh, he has been working extensively on building algorithmic foundations of randomized methods in convex optimization, especially randomized coordinate descent algorithms and stochastic gradient descent methods. These methods are well suited for optimization problems described by big data and have applications in fields such as machine learning, signal processing and data science. Richtarik is the co-inventor of an algorithm generalizing the randomized Kaczmarz method for solving a system of linear equations, contributed to the invention of federated learning, and co-developed a stochastic variant of the Newton's method.

Awards and distinctions 2020, Due to his Hirsch index of 40 or more, he belongs among top 0.05% of computer scientists. 2016, SIGEST Award (jointly with Olivier Fercoq) of the Society for Industrial and Applied Mathematics 2016, EPSRC Early Career Fellowship in Mathematical Sciences 2015, EUSA Best Research or Dissertation Supervisor Award (2nd place) 2014, Plenary Talk at 46th Conference of Slovak Mathematicians

Bibliography Peter Richtarik & Martin Takac (2012). "Efficient serial and parallel coordinate descent methods for huge-scale truss topology design". Operations Research Proceedings 2011. Operations Research Proceedings. Springer-Verlag. pp. 27–32. doi:10.1007/978-3-642-29210-1_5. ISBN 978-3-642-29209-5.{{cite news}}: CS1 maint: periodical has ISBN (link) Peter Richtarik & Martin Takac (2014). "Iteration complexity of randomized block-coordinate descent methods for minimizing a composite function". Mathematical Programming. 144 (1). Springer: 1–38. arXiv:1107.2848. doi:10.1007/s10107-012-0614-z. S2CID 254137101. Olivier Fercoq & Peter Richtarik (2015). "Accelerated, parallel and proximal coordinate descent". SIAM Journal on Optimization. 25 (4): 1997–2023. arXiv:1312.5799. doi:10.1137/130949993. S2CID 8068556. Dominik Csiba; Zheng Qu; Peter Richtarik (2015). "Stochastic Dual Coordinate Ascent with Adaptive Probabilities" (pdf). Proceedings of the 32nd International Conference on Machine Learning. pp. 674–683. Robert M Gower & Peter Richtarik (2015). "Randomized Iterative Methods for Linear Systems". SIAM Journal on Matrix Analysis and Applications. 36 (4): 1660–1690. arXiv:1506.03296. doi:10.1137/15M1025487. hdl:20.500.11820/5c673b9e-8cf3-482c-8602-da8abcb903dd. S2CID 8215294. Peter Richtarik & Martin Takac (2016). "Parallel coordinate descent methods for big data optimization". Mathematical Programming. 156 (1): 433–484. doi:10.1007/s10107-015-0901-6. hdl:20.500.11820/a5649cad-b6b8-4ccc-9ca2-b368131dcbe5. S2CID 254133277. Zheng Qu & Peter Richtarik (2016). "Coordinate descent with arbitrary sampling I: algorithms and complexity". Optimization Methods and Software. 31 (5): 829–857. arXiv:1412.8060. doi:10.1080/10556788.2016.1190360. S2CID 2636844. Zheng Qu & Peter Richtarik (2016). "Coordinate descent with arbitrary sampling II: expected separable overapproximation". Optimization Methods and Software. 31 (5): 858–884. arXiv:1412.8063. doi:10.1080/10556788.2016.1190361. S2CID 11048560. Zheng Qu; Peter Richtarik; Martin Takac; Olivier Fercoq (2016). "SDNA: Stochastic Dual Newton Ascent for Empirical Risk Minimization" (pdf). Proceedings of the 33rd International Conference on Machine Learning. pp. 1823–1832. Zeyuan Allen-Zhu; Zheng Qu; Peter Richtarik; Yang Yuan (2016). "Even faster accelerated coordinate descent using non-uniform sampling" (pdf). Proceedings of the 33rd International Conference on Machine Learning. pp. 1110–1119. Dominik Csiba & Peter Richtarik (2016). "Importance sampling for minibatches". arXiv:1602.02283 [cs.LG]. Dominik Csiba & Peter Richtarik (2016). "Coordinate descent face-off: primal or dual?". arXiv:1605.08982 [math.OC].

References

External links Richtarik's professional web page Richtarik's Google Scholar profile

Worked examples

Example 1 — a first encounter with Peter Richtarik

Start with the simplest possible case. Write down what Peter Richtarik claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In mathematics, the smallest case is usually a single object, a single equation or a single measurement. Check that every symbol or term in your sentence has a meaning in that case.

Example 2 — changing one variable

Take the situation from Example 1 and change exactly one quantity: double it, halve it, or set it to zero. Predict what should happen to Peter Richtarik before you calculate. Comparing your prediction with the result is the fastest way to find out whether you understand the idea or only the words.

Example 3 — an exam-style question

Typical questions about Peter Richtarik ask you to (a) state it precisely, (b) apply it to given data, and (c) explain a limitation. Practise writing all three answers in under five minutes; the third part is what separates a full-mark answer from an average one.

Applications of Peter Richtarik

In research: Peter Richtarik appears in mathematics research whenever the underlying quantities have to be modelled precisely. Papers usually cite it as a starting assumption and then explore where it breaks down.
In technology and industry: Engineering practice reuses Peter Richtarik in design rules, simulations and safety margins. Knowing the idea lets you read a specification sheet and understand why the numbers look the way they do.
In the classroom: Peter Richtarik is common in secondary-school and first-year university syllabi. It links to neighbouring topics Cornell University alumni, Living people, Slovak mathematicians, so understanding it makes those chapters shorter.
In everyday life: Look for Peter Richtarik outside the textbook — in sport, cooking, traffic, electronics or the sky above you. An example you found yourself is remembered far longer than one you were given.

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How to study Peter Richtarik in 20 minutes

Read the reference excerpt below once, without taking notes.
Close the page and write down what Peter Richtarik means in your own words.
Compare your version with the excerpt and mark what you missed.
Work through the three examples above with pen and paper.
Explain Peter Richtarik out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is Peter Richtarik in simple terms?

Why does Peter Richtarik matter?

Because it connects several mathematics ideas at once: it gives you a definition you can apply, a quantity you can calculate, and a way to check whether a result is plausible.

How should I study Peter Richtarik?

Read the excerpt, restate it from memory, then work through the examples and applications listed on this page. The five-step study plan above takes about twenty minutes.

What does this page cover?

It gives you a compact reference excerpt plus original lgStudy explanations, examples, applications and study material on Peter Richtarik.