We perform an average analysis of the Grassmann condition number C (A) for the homogeneous convex feasibility problem ∃x ∈ C \ 0 : Ax = 0, where C ⊂ R may be any regular cone. This in particular includes the cases of linear programming, second-order programming, and semidefinite programming. We thus give the first average analysis of convex programming, which is not restricted to linear program...

In this paper rst we prove four fundamental theorems of the alternative, called scaling dualities, characterizing exact and approximate solvability of four signi cant conic problems in nite dimensional spaces, de ned as: homogeneous programming (HP), scaling problem (SP), homogeneous scaling problem (HSP), and algebraic scaling problem (ASP). Let be a homogeneous function of degree p > 0, K a p...

In this paper first we prove four fundamental theorems of the alternative, called scaling dualities, characterizing exact and approximate solvability of four significant conic problems in finite dimensional spaces, defined as: homogeneous programming (HP), scaling problem (SP), homogeneous scaling problem (HSP), and algebraic scaling problem (ASP). Let φ be a homogeneous function of degree p > ...

For a proper cone K ⊂ Rn and its dual cone K∗ the complementary slackness condition xT s = 0 defines an n-dimensional manifold C(K) in the space { (x, s) | x ∈ K, s ∈ K∗ }. When K is a symmetric cone, this fact translates to a set of n linearly independent bilinear identities (optimality conditions) satisfied by every (x, s) ∈ C(K). This proves to be very useful when optimizing over such cones,...

In this paper first we prove four fundamental theorems of the alternative, called scaling dualities, characterizing exact and approximate solvability of four significant conic problems in finite dimensional spaces, defined as: homogeneous programming (HP), scaling problem (SP), homogeneous scaling problem (HSP), and algebraic scaling problem (ASP). Let φ be a homogeneous function of degree p > ...

An algorithm for monotropic piecewise quadratic programming is developed. It converges to an exact solution in finitely many iterations and can be thought of as an extension of the simplex method for convex programming and the active set method for quadratic programming. Computational results show that solving a piecewise quadratic program is not much harder than solving a quadratic program of ...