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  2. Lagrange multiplier - Wikipedia

    en.wikipedia.org/wiki/Lagrange_multiplier

    The Lagrange multiplier theorem states that at any local maximum (or minimum) of the function evaluated under the equality constraints, if constraint qualification applies (explained below), then the gradient of the function (at that point) can be expressed as a linear combination of the gradients of the constraints (at that point), with the ...

  3. Lagrangian relaxation - Wikipedia

    en.wikipedia.org/wiki/Lagrangian_relaxation

    These added costs are used instead of the strict inequality constraints in the optimization. In practice, this relaxed problem can often be solved more easily than the original problem. The problem of maximizing the Lagrangian function of the dual variables (the Lagrangian multipliers) is the Lagrangian dual problem .

  4. Pontryagin's maximum principle - Wikipedia

    en.wikipedia.org/wiki/Pontryagin's_maximum_Principle

    [a] These necessary conditions become sufficient under certain convexity conditions on the objective and constraint functions. [ 1 ] [ 2 ] The maximum principle was formulated in 1956 by the Russian mathematician Lev Pontryagin and his students, [ 3 ] [ 4 ] and its initial application was to the maximization of the terminal speed of a rocket. [ 5 ]

  5. Augmented Lagrangian method - Wikipedia

    en.wikipedia.org/wiki/Augmented_Lagrangian_method

    Augmented Lagrangian methods are a certain class of algorithms for solving constrained optimization problems. They have similarities to penalty methods in that they replace a constrained optimization problem by a series of unconstrained problems and add a penalty term to the objective, but the augmented Lagrangian method adds yet another term designed to mimic a Lagrange multiplier.

  6. Karush–Kuhn–Tucker conditions - Wikipedia

    en.wikipedia.org/wiki/Karush–Kuhn–Tucker...

    Consider the following nonlinear optimization problem in standard form: . minimize () subject to (),() =where is the optimization variable chosen from a convex subset of , is the objective or utility function, (=, …,) are the inequality constraint functions and (=, …,) are the equality constraint functions.

  7. Kim Kardashian Has Holiday Photo Booth Fun with Daughters ...

    www.aol.com/kim-kardashian-holiday-photo-booth...

    Kim Kardashian is having a picture-perfect holiday season! On Monday, Dec. 30, the SKIMS founder, 44, shared a selection of sweet images from her festive family time.. Among them were several ...

  8. Retiree Mental Health Crisis: The Best & Worst States for ...

    www.aol.com/retiree-mental-health-crisis-best...

    Every generation views their health and wellness differently. For older Americans, mental health diagnoses are becoming more prevalent. Between 2019 and 2023, the 65+ age group collectively ...

  9. Corgi Puppy Mistakes Ornaments For Toy Balls During Her Very ...

    www.aol.com/corgi-puppy-mistakes-ornaments-toy...

    OMG—what a little cutie! Marshmallow is having a ball (pun intended) chasing the Christmas ornaments, and her family gets major brownie points for letting the puppy play.It's a plastic ornament ...