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  2. Algebra - Wikipedia

    en.wikipedia.org/wiki/Algebra

    The same principles also apply to systems of equations with more variables, with the difference being that the equations do not describe lines but higher dimensional figures. For instance, equations with three variables correspond to planes in three-dimensional space, and the points where all planes intersect solve the system of equations. [52]

  3. P versus NP problem - Wikipedia

    en.wikipedia.org/wiki/P_versus_NP_problem

    NP-hard problems are those at least as hard as NP problems; i.e., all NP problems can be reduced (in polynomial time) to them. NP-hard problems need not be in NP; i.e., they need not have solutions verifiable in polynomial time. For instance, the Boolean satisfiability problem is NP-complete by the Cook–Levin theorem, so any instance of any ...

  4. Mathematics - Wikipedia

    en.wikipedia.org/wiki/Mathematics

    For getting an algorithm that can be implemented and can solve systems of polynomial equations and inequalities, George Collins introduced the cylindrical algebraic decomposition that became a fundamental tool in real algebraic geometry. [123]

  5. Calculus - Wikipedia

    en.wikipedia.org/wiki/Calculus

    Calculus is also used to find approximate solutions to equations; in practice, it is the standard way to solve differential equations and do root finding in most applications. Examples are methods such as Newton's method, fixed point iteration, and linear approximation.

  6. Isaac Newton - Wikipedia

    en.wikipedia.org/wiki/Isaac_Newton

    Building the design, the first known functional reflecting telescope, today known as a Newtonian telescope, involved solving the problem of a suitable mirror material and shaping technique. [87] He grounded his own mirrors out of a custom composition of highly reflective speculum metal , using Newton's rings to judge the quality of the optics ...

  7. Support vector machine - Wikipedia

    en.wikipedia.org/wiki/Support_vector_machine

    To avoid solving a linear system involving the large kernel matrix, a low-rank approximation to the matrix is often used in the kernel trick. Another common method is Platt's sequential minimal optimization (SMO) algorithm, which breaks the problem down into 2-dimensional sub-problems that are solved analytically, eliminating the need for a ...

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