In this paper a method is presented for the estimation of interior orientation parameters and lens distortion using measurements of lines in a single image and constraints derived from a priori information on the orientation of the related object lines. The interior orientation parameters consist of the location of the principle point and the focal length. Radial lens distortion is modeled with...

This paper proposes an infeasible interior-point algorithm with full Nesterov-Todd step for second-order cone programming, which is an extension of the work of Roos (SIAM J. Optim., 16(4):1110–1136, 2006). The polynomial bound coincides with that of infeasible interior-point methods for linear programming, namely, O(l log l/ε).

This paper proposes an infeasible interior-point algorithm with full Nesterov-Todd step for semidefinite programming, which is an extension of the work of Roos (SIAM J. Optim., 16(4):1110– 1136, 2006). The polynomial bound coincides with that of infeasible interior-point methods for linear programming, namely, O(n log n/ε).

We present a full-Newton step infeasible interior-point algorithm. It is shown that at most O(n) (inner) iterations suffice to reduce the duality gap and the residuals by the factor 1 e . The bound coincides with the best known bound for infeasible interior-point algorithms. It is conjectured that further investigation will improve the above bound to O( √ n).

Most existing interior-point methods for a linear complementarity problem (LCP) require the existence of a strictly feasible point to guarantee that the iterates are bounded. Based on a regularized central path, we present an infeasible interior-point algorithm for LCPs without requiring the strict feasibility condition. The iterates generated by the algorithm are bounded when the problem is a ...

Many practical optimization problems involve nonsmooth (that is, not necessarily differentiable) functions of hundreds or thousands of variables with various constraints. In this paper, we describe a new efficient adaptive limited memory interior point bundle method for large, possible nonconvex, nonsmooth inequality constrained optimization. The method is a hybrid of the nonsmooth variable met...

We present a generalization of a homogeneous self-dual linear programming (LP) algorithm to solving the monotone complementarity problem (MCP). The algorithm does not need to use any \big-M" parameter or two-phase method, and it generates either a solution converging towards feasibility and complementarity simultaneously or a certiicate proving infeasibility. Moreover, if the MCP is polynomiall...

We propose a relaxation scheme for mathematical programs with equilibrium constraints (MPECs). In contrast to previous approaches, our relaxation is two-sided: both the complementarity and the nonnegativity constraints are relaxed. The proposed relaxation update rule guarantees (under certain conditions) that the sequence of relaxed subproblems will maintain a strictly feasible interior—even in...

We analyze the canonical market game. There are e commodities, a single inside money, L markets in which commodities are exchanged for inside money, and n consumers. Each consumer’s strategy is the nonnegative vector of his commodity offers and his money bids. Given endowments and sufftciently large offers, the set of interior Nash equilibrium strategies is finite and non-empty. Hence the set o...