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Computational Geometry   (Mathematica Guide)
Mathematica's strengths in algebraic computation and graphics as well as numerics combine to bring unprecedented flexibility and power to geometric computation. Making ...
MaxIterations   (Built-in Mathematica Symbol)
MaxIterations is an option that specifies the maximum number of iterations that should be tried in various built-in functions and algorithms.
Assumptions and Domains   (Mathematica Guide)
Mathematica has a flexible system for specifying arbitrary symbolic assumptions about variables. It uses a wide range of sophisticated algorithms to infer the consequences of ...
Numerical Calculations   (Mathematica Tutorial)
Exact symbolic results are usually very desirable when they can be found. In many calculations, however, it is not possible to get symbolic results. In such cases, you must ...
Calculus   (Mathematica Guide)
In calculus even more than other areas, Mathematica packs centuries of mathematical development into a small number of exceptionally powerful functions. Continually enhanced ...
Numerical Evaluation & Precision   (Mathematica Guide)
In two decades of intense algorithmic development, Mathematica has established a new level of numerical computation. Particularly notable are its many original highly ...
Polynomial Systems   (Mathematica Guide)
Mathematica's handling of polynomial systems is a tour de force of algebraic computation. Building on mathematical results spanning more than a century, Mathematica for the ...
Graph Programming   (Mathematica Guide)
By providing a completely extensible set of vertex and edge properties, you can make graphs represent much more than the structural information embodied in their topology. ...
Optimization   (Mathematica Guide)
Integrated into Mathematica is a full range of state-of-the-art local and global optimization techniques, both numeric and symbolic, including constrained nonlinear ...
Introduction to Constrained ...   (Mathematica Tutorial)
Constrained optimization problems are problems for which a function f(x) is to be minimized or maximized subject to constraints Φ(x). Here f:^n  is called the objective ...
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