“For other problems of interest, we can use so-called math mountains to represent a quantity we’d like to maximize or minimize, given our choices, and then use optimization methods to scale or descend ...

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Mathematical optimization and machine learning are ... Fundamentally, both are based on deep mathematics and are shining examples of how mathematics — along with data and computers — can ...

Optimization without calculus; geometric programming; convex sets and convex functions; review of linear algebra; linear programming and the simplex method; convex programming; iterative ...

"Instead of formally defining the optimization problem and deriving the ... to produce more accurate results when tested against math problem data sets. (This technique became widely known in ...

In Part 1 of this series on optimization and recovery, we considered two limitations of optimization processes and the light they shed on pseudo-recovery. Let’s now think more about what the ...

A century ago, the great mathematician David Hilbert posed a probing question in pure mathematics. A recent advance in optimization theory is bringing Hilbert’s work into a world of self-driving cars.

By making optimization the new maximization. Now is the time for a new financial math that simplifies what should become our common goal: O=FR+(PE-NE), or “Optimization equals financial returns ...

Optimization has its limits ... And, in the interplay of pure and applied math, here the practical, real-world need is motivating new mathematics? Exactly. The engineering of machine learning ...