name: lagrangian version: 0.6.0.1 synopsis: Solve Lagrange multiplier problems description: Numerically solve convex Lagrange multiplier problems with conjugate gradient descent. . For some background on the method of Lagrange multipliers checkout the wikipedia page . Here is an example from the Wikipedia page on Lagrange multipliers Maximize f(x, y) = x + y, subject to the constraint x^2 + y^2 = 1 . @ \> maximize 0.00001 (\\[x, y] -> x + y) [(\\[x, y] -> x^2 + y^2) \<=\> 1] 2 Right ([0.707,0.707], [-0.707]) @ . For more information look here: . For example, to find the maximum entropy with the constraint that the probabilities sum to one. . @ \> maximize 0.00001 (negate . sum . map (\\x -> x * log x)) [sum \<=\> 1] 3 Right ([0.33, 0.33, 0.33], [-0.09]) @ . The first elements of the result pair are the arguments for the objective function at the maximum. The second elements are the Lagrange multipliers. . homepage: http://github.com/jfischoff/lagrangian license: BSD3 license-file: LICENSE author: (c) Jonathan Fischoff 2012-2014, (c) Eric Pashman 2014 maintainer: jonathangfischoff@gmail.com -- copyright: category: Math build-type: Simple cabal-version: >=1.8 library exposed-modules: Numeric.AD.Lagrangian other-modules: Numeric.AD.Lagrangian.Internal ghc-options: -Wall build-depends: base >=4.5 && < 5, nonlinear-optimization ==0.3.*, vector ==0.10.*, ad >= 4 && < 5, hmatrix >= 0.14 && < 0.17 hs-source-dirs: src Test-Suite tests Hs-Source-Dirs: src, tests type: exitcode-stdio-1.0 main-is: Main.hs build-depends: base >=4.5 && < 5, nonlinear-optimization ==0.3.*, vector ==0.10.*, ad >= 4 && <5, hmatrix >= 0.14 && < 0.17, test-framework ==0.8.*, test-framework-hunit ==0.3.*, test-framework-quickcheck2 ==0.3.*, HUnit == 1.2.*