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  2. Multi-objective linear programming - Wikipedia

    en.wikipedia.org/wiki/Multi-objective_linear...

    This term is misleading because a single efficient point can be already obtained by solving one linear program, such as the linear program with the same feasible set and the objective function being the sum of the objectives of MOLP. [4] More recent references consider outcome set based solution concepts [5] and corresponding algorithms.

  3. Linear programming - Wikipedia

    en.wikipedia.org/wiki/Linear_programming

    More formally, linear programming is a technique for the optimization of a linear objective function, subject to linear equality and linear inequality constraints. Its feasible region is a convex polytope , which is a set defined as the intersection of finitely many half spaces , each of which is defined by a linear inequality.

  4. Simplex algorithm - Wikipedia

    en.wikipedia.org/wiki/Simplex_algorithm

    The storage and computation overhead is such that the standard simplex method is a prohibitively expensive approach to solving large linear programming problems. In each simplex iteration, the only data required are the first row of the tableau, the (pivotal) column of the tableau corresponding to the entering variable and the right-hand-side.

  5. Multi-objective optimization - Wikipedia

    en.wikipedia.org/wiki/Multi-objective_optimization

    Multi-objective optimization or Pareto optimization (also known as multi-objective programming, vector optimization, multicriteria optimization, or multiattribute optimization) is an area of multiple-criteria decision making that is concerned with mathematical optimization problems involving more than one objective function to be optimized simultaneously.

  6. Reduced cost - Wikipedia

    en.wikipedia.org/wiki/Reduced_cost

    In linear programming, reduced cost, or opportunity cost, is the amount by which an objective function coefficient would have to improve (so increase for maximization problem, decrease for minimization problem) before it would be possible for a corresponding variable to assume a positive value in the optimal solution.

  7. HiGHS optimization solver - Wikipedia

    en.wikipedia.org/wiki/HiGHS_optimization_solver

    HiGHS is open-source software to solve linear programming (LP), mixed-integer programming (MIP), and convex quadratic programming (QP) models. [1] Written in C++ and published under an MIT license, HiGHS provides programming interfaces to C, Python, Julia, Rust, JavaScript, Fortran, and C#. It has no external dependencies.

  8. These Charming Vintage Cookie Jars Are Worth Top Dollar

    www.aol.com/charming-vintage-cookie-jars-worth...

    Shawnee Pottery, an American pottery company that operated from 1937 to 1961, is known for its eye-catching designs. Glazed inside and out, some Shawnee jars — like this Shawnee cottage cookie ...

  9. TI-36 - Wikipedia

    en.wikipedia.org/wiki/TI-36

    Vector: 3 editable tables, preset last matrix/vector result, vector arithmetic (addition, subtraction, scalar multiplication, matrix-vector multiplication (vector interpreted as column)), dot product, cross product; Polynomial solver: 2nd/3rd degree solver. Linear equation solver: 2x2 and 3x3 solver. Base-N operations: XNOR, NAND; Expression ...