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Simplify   (Built-in Mathematica Symbol)
Simplify[expr] performs a sequence of algebraic and other transformations on expr, and returns the simplest form it finds. Simplify[expr, assum] does simplification using ...
NMinimize, NMaximize, Minimize, and Maximize employ global optimization algorithms, and are thus suitable when a global optimum is needed. Minimize and Maximize can find ...
Date and Time Functions   (Mathematica Tutorial)
Finding the date and time. This gives the current date and time. The Mathematica DateList function returns whatever your computer system gives as the current date and time. ...
Solving Equations   (Mathematica Tutorial)
An expression like x^2+2x-7==0 represents an equation in Mathematica. You will often need to solve equations like this, to find out for what values of x they are true. This ...
Applications   (CUDALink Tutorial)
Because GPUs are SIMD machines, to exploit CUDA's potential you must pose the problem in an SIMD manner. Computation that can be partitioned in such a way that each thread ...
Complex Polynomial Systems   (Mathematica Tutorial)
The Mathematica functions Reduce, Resolve, and FindInstance allow you to solve a wide variety of problems that can be expressed in terms of equations and inequalities. The ...
Specific Compilers   (C Compiler Driver Package Tutorial)
The CCompilerDriver package lets you work with C compilers that are installed on your computer. It lets you build executables, libraries, and object files from C source code. ...
Launching and Connecting   (Parallel Package Tutorial)
Mathematica can run parallel kernels in a number of different ways; locally on the same machine or remote on other machines connected in a network. Furthermore, the network ...
ListPlot3D   (Built-in Mathematica Symbol)
ListPlot3D[array] generates a three-dimensional plot of a surface representing an array of height values. ListPlot3D[{{x_1, y_1, z_1}, {x_2, y_2, z_2}, ...}] generates a plot ...
SmoothKernelDistribution   (Built-in Mathematica Symbol)
SmoothKernelDistribution[{x_1, x_2, ...}] represents a smooth kernel distribution based on the data values x_i.SmoothKernelDistribution[{{x_1, y_1, ...}, {x_2, y_2, ...}, ...
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