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  2. Gekko (optimization software) - Wikipedia

    en.wikipedia.org/wiki/Gekko_(optimization_software)

    GEKKO is an extension of the APMonitor Optimization Suite but has integrated the modeling and solution visualization directly within Python. A mathematical model is expressed in terms of variables and equations such as the Hock & Schittkowski Benchmark Problem #71 [ 2] used to test the performance of nonlinear programming solvers.

  3. Greedy algorithm - Wikipedia

    en.wikipedia.org/wiki/Greedy_algorithm

    A greedy algorithm is used to construct a Huffman tree during Huffman coding where it finds an optimal solution. In decision tree learning, greedy algorithms are commonly used, however they are not guaranteed to find the optimal solution. One popular such algorithm is the ID3 algorithm for decision tree construction.

  4. Boolean satisfiability problem - Wikipedia

    en.wikipedia.org/wiki/Boolean_satisfiability_problem

    The Boolean satisfiability problem (SAT) is, given a formula, to check whether it is satisfiable. This decision problem is of central importance in many areas of computer science, including theoretical computer science, complexity theory, [3][4] algorithmics, cryptography [5][6] and artificial intelligence. [7][additional citation (s) needed]

  5. Nonlinear programming - Wikipedia

    en.wikipedia.org/wiki/Nonlinear_programming

    Nonlinear programming. In mathematics, nonlinear programming (NLP) is the process of solving an optimization problem where some of the constraints are not linear equalities or the objective function is not a linear function. An optimization problem is one of calculation of the extrema (maxima, minima or stationary points) of an objective ...

  6. Knapsack problem - Wikipedia

    en.wikipedia.org/wiki/Knapsack_problem

    Definition. The most common problem being solved is the 0-1 knapsack problem, which restricts the number of copies of each kind of item to zero or one. Given a set of items numbered from 1 up to , each with a weight and a value , along with a maximum weight capacity , subject to and . Here represents the number of instances of item to include ...

  7. Constraint satisfaction problem - Wikipedia

    en.wikipedia.org/.../Constraint_satisfaction_problem

    Constraint satisfaction problem. Constraint satisfaction problems (CSPs) are mathematical questions defined as a set of objects whose state must satisfy a number of constraints or limitations. CSPs represent the entities in a problem as a homogeneous collection of finite constraints over variables, which is solved by constraint satisfaction ...

  8. Benders decomposition - Wikipedia

    en.wikipedia.org/wiki/Benders_decomposition

    Benders decomposition (or Benders' decomposition) is a technique in mathematical programming that allows the solution of very large linear programming problems that have a special block structure. This block structure often occurs in applications such as stochastic programming as the uncertainty is usually represented with scenarios.

  9. Gurobi Optimizer - Wikipedia

    en.wikipedia.org/wiki/Gurobi_Optimizer

    Gurobi Optimizer is a prescriptive analytics platform and a decision-making technology developed by Gurobi Optimization, LLC. The Gurobi Optimizer (often referred to as simply, “Gurobi”) is a solver, since it uses mathematical optimization to calculate the answer to a problem. Gurobi is included in the Q1 2022 inside BIGDATA “Impact 50 ...