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  2. Farkas' lemma - Wikipedia

    en.wikipedia.org/wiki/Farkas'_lemma

    Farkas' lemma is the key result underpinning the linear programming duality and has played a central role in the development of mathematical optimization (alternatively, mathematical programming). It is used amongst other things in the proof of the Karush–Kuhn–Tucker theorem in nonlinear programming . [ 2 ]

  3. Transportation theory (mathematics) - Wikipedia

    en.wikipedia.org/wiki/Transportation_theory...

    Monge's formulation of the optimal transportation problem can be ill-posed, because sometimes there is no satisfying () =: this happens, for example, when is a Dirac measure but is not. We can improve on this by adopting Kantorovich's formulation of the optimal transportation problem, which is to find a probability measure γ {\displaystyle ...

  4. Linear programming - Wikipedia

    en.wikipedia.org/wiki/Linear_programming

    Linear programming (LP), also called linear optimization, is a method to achieve the best outcome (such as maximum profit or lowest cost) in a mathematical model whose requirements and objective are represented by linear relationships. Linear programming is a special case of mathematical programming (also known as mathematical optimization).

  5. LP-type problem - Wikipedia

    en.wikipedia.org/wiki/LP-type_problem

    The discovery of linear time algorithms for linear programming and the observation that the same algorithms could in many cases be used to solve geometric optimization problems that were not linear programs goes back at least to Megiddo (1983, 1984), who gave a linear expected time algorithm for both three-variable linear programs and the ...

  6. Linear complementarity problem - Wikipedia

    en.wikipedia.org/wiki/Linear_complementarity_problem

    Updated and free PDF version at Katta G. Murty's website. Archived from the original on 2010-04-01. Taylor, Joshua Adam (2015). Convex Optimization of Power Systems. Cambridge University Press. ISBN 9781107076877. Terlaky, Tamás; Zhang, Shu Zhong (1993). "Pivot rules for linear programming: A Survey on recent theoretical developments".

  7. Weak formulation - Wikipedia

    en.wikipedia.org/wiki/Weak_formulation

    This is a formulation of the Lax–Milgram theorem which relies on properties of the symmetric part of the bilinear form. It is not the most general form. It is not the most general form. Let V {\displaystyle V} be a real Hilbert space and a ( ⋅ , ⋅ ) {\displaystyle a(\cdot ,\cdot )} a bilinear form on V {\displaystyle V} , which is

  8. Linear-fractional programming - Wikipedia

    en.wikipedia.org/wiki/Linear-fractional_programming

    In mathematical optimization, linear-fractional programming (LFP) is a generalization of linear programming (LP). Whereas the objective function in a linear program is a linear function, the objective function in a linear-fractional program is a ratio of two linear functions. A linear program can be regarded as a special case of a linear ...

  9. Polarization identity - Wikipedia

    en.wikipedia.org/wiki/Polarization_identity

    In linear algebra, a branch of mathematics, the polarization identity is any one of a family of formulas that express the inner product of two vectors in terms of the norm of a normed vector space. If a norm arises from an inner product then the polarization identity can be used to express this inner product entirely in terms of the norm.