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 Equality-constrained optimization can be performed without Lagrange mulitpliers, using differential forms. See "Differential forms for constrained max-min problems: eliminating Lagrange multipliers", College Mathematics Journal 29 (November 1998), no. 5, 387--396, by Frank Zizza The differential forms condition to minimize or maximize f subject to g1 == 0, g2 == 0, etc. is df ^ dg1 ^ dg2 ... == 0 where df, dg1, dg2, etc. are differentials of f, g1, g2, etc. and ^ is the wedge product. This operation can be carried out in Mathematica using the TensorWedge function. For example, considering minimizing x + y subject to x^2+ y^2==1 In[1]:= f = x + y; g = x^2 + y^2 - 1; vars = {x, y}; In[4]:= df = Grad[f, vars]; dg = Grad[g, vars]; In[7]:= w = SymmetrizedArrayRules[TensorWedge[df, dg]] Out[7]= {{1, 2} -> -2 x + 2 y, {_, _} -> 0} In[10]:= Reduce[w[[All, 2]] == 0 && g == 0, vars, Backsubstitution -> True] Out[10]= (x == -(1/Sqrt[2]) && y == -(1/Sqrt[2])) || (x == 1/Sqrt[2] && y == 1/Sqrt[2])  The Lagrange multiplier approach would be In[11]:= Reduce[df == \[Lambda] dg && g == 0, vars, Backsubstitution -> True] Out[11]= (\[Lambda] == -(1/Sqrt[2]) && x == -(1/Sqrt[2]) && y == -(1/Sqrt[2])) || (\[Lambda] == 1/Sqrt[2] && x == 1/Sqrt[2] && y == 1/Sqrt[2])