astronomy

Reuven Rubinstein

Reuven Rubinstein is a astronomy 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 Reuven Rubinstein rather than just read about it. In short: Reuven Rubinstein (Hebrew: ראובן רובינשטיין; 1938–2012) was an Israeli scientist known for his contributions to Monte Carlo simulation, applied probability, stochastic modeling, and stochastic optimization, having authored more than one hundred papers and six books. During his career, Rubinstein made fundamental and important contributions in these fields and advanced the theory and application of adaptive importanc…

Key takeaways

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

Reference excerpt

Reuven Rubinstein (Hebrew: ראובן רובינשטיין; 1938–2012) was an Israeli scientist known for his contributions to Monte Carlo simulation, applied probability, stochastic modeling, and stochastic optimization, having authored more than one hundred papers and six books. During his career, Rubinstein made fundamental and important contributions in these fields and advanced the theory and application of adaptive importance sampling, rare-event simulation, stochastic optimization, sensitivity analysis of simulation-based models, the splitting method, and counting problems concerning NP-complete problems. He is well known as the founder of several breakthrough methods, such as the score-function method, the stochastic counterpart method, and the cross-entropy method, which have numerous applications in combinatorial optimization and simulation.

Early life and education Rubinstein was born on August 25, 1938, in Kaunas, Lithuania. In 1960, he received a BSc (unknown subject) and MSc (Electrical Engineering) from the Kaunas Polytechnical Institute. In 1969, he received a DSc in Operations Research from the Riga Polytechnical Institute.

Career and awards His citation index is in the top 5% among his colleagues in operations research and management sciences. His 1981 book "Simulation and the Monte Carlo Method", Wiley (second edition 2008 and third edition 2017, with Dirk Kroese) alone has over 10,000 citations. He has held visiting positions in various research institutes, including the University of Illinois Urbana-Champaign, Columbia University, Harvard University, Stanford University, IBM, and Bell Laboratories. He has given invited and plenary lectures in many international conferences around the globe. In 2010 Prof. Rubinstein won the INFORMS Simulation Society's highest prize—the Lifetime Professional Achievement Award (LPAA), which recognizes scholars who have made fundamental contributions to the field of simulation that persist over most of a professional career. In 2011 Reuven Rubinstein won from the Operations Research Society of Israel (ORSIS) the Lifetime Professional Award (LPA), which recognizes scholars who have made fundamental contributions to the field of operations research over most of a professional career and constitutes ORSIS's highest award.

Publications

Books Rubinstein, R.Y., "Simulation and the Monte Carlo Method", Wiley, 1981. Rubinstein, R.Y., "Monte Carlo Optimization, Simulation and Sensitivity of Queueing Networks", Wiley, 1986. Rubinstein, R.Y., and A. Shapiro, "Discrete Event Systems: Sensitivity Analysis and Stochastic Optimization", Wiley, 1993. Melamed, B., and R.Y. Rubinstein, "Modern Simulation and Modeling", Wiley, 1998. Rubinstein, R.Y., and D.P. Kroese, "The Cross-Entropy Method: a Unified Approach to Combinatorial Optimization, Monte-Carlo Simulation and Machine Learning", Springer, 2004. Rubinstein, R.Y., and D.P. Kroese, "Simulation and the Monte Carlo Method", Second Edition, Wiley, 2008. Rubinstein, R.Y., Rider, A., and R. Vaisman, "Fast Sequential Monte Carlo Methods for Counting and Optimization", Wiley, 2014. Rubinstein, R.Y., and D.P. Kroese, "Simulation and the Monte Carlo Method", Third Edition, Wiley, 2017.

Journal articles Rubinstein, R.Y., "The cross-entropy method for combinatorial and continuous optimization", Methodology and Computing in Applied Probability, 2, 127–190, 1999. Rubinstein R.Y., "Randomized algorithms with splitting: Why the classic randomized algorithms do not work and how to make them work", Methodology and Computing in Applied Probability, 2009. doi:10.1007/s11009-009-9126-6

References

Illustrations

Worked examples

Example 1 — a first encounter with Reuven Rubinstein

Start with the simplest possible case. Write down what Reuven Rubinstein claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In astronomy, 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 Reuven Rubinstein 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 Reuven Rubinstein 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 Reuven Rubinstein

In research: Reuven Rubinstein appears in astronomy 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 Reuven Rubinstein 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: Reuven Rubinstein is common in secondary-school and first-year university syllabi. It links to neighbouring topics 1938 births, 2012 deaths, Columbia University staff, so understanding it makes those chapters shorter.
In everyday life: Look for Reuven Rubinstein 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 Reuven Rubinstein in 20 minutes

Read the reference excerpt below once, without taking notes.
Close the page and write down what Reuven Rubinstein 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 Reuven Rubinstein out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is Reuven Rubinstein in simple terms?

Why does Reuven Rubinstein matter?

Because it connects several astronomy 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 Reuven Rubinstein?

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 Reuven Rubinstein.