Conic dual problem Created 2026-10-06 Updated 2026-10-14
A conic dual problem searches for a lower bound on a conic optimization problem. A feasible point exhibits an attainable cost; a feasible dual variable explains why every allowed decision costs at least a specified amount. If the two costs agree, the pair certifies a global minimum without relying on how the points were found.
Fix a real matrix , vectors , , and a closed convex cone . With unrestricted , use the conventionwhere the dual cone is . The conic dual problem uses because it must turn every allowed slack variable into a nonnegative dot product. Taking gives componentwise linear inequalities; taking a second-order cone gives a Euclidean norm constraint. A self-dual cone has , but this equality does not guarantee a feasible point or strong duality.
The Lagrangian supplies a short derivation. For ,Its infimum over unrestricted is when , and otherwise. Maximizing the resulting Lagrange dual function gives the stated conic dual problem. The minus sign matches the convention .
Past exam of the mathematics course of the University of Cambridge 2013 iii Paper 62 5 a Solution Created 2026-10-03 Updated 2026-10-07
Use the nonnegative-pairing dual cone . The Lagrangian is for . Its infimum over unrestricted is finite exactly when . Therefore the conic dual problem isThe final equality uses the self-dual cone assumption.
For the canonical self-concordant barrier, use its logarithmically homogeneous barrier normalizationAt parameter , the primal barrier problem isWrite , the Legendre dual cone barrier. The matching dual barrier problem isDefining the dual barrier this way is valid generally; self-duality of the cone alone does not assert that an arbitrarily selected barrier equals its Legendre dual.
The joint central path characterization isThe primal stationarity equation is , exactly the dual feasibility equation after defining . For the dual relation, logarithmic homogeneity gives , so . Thus dual stationarity is , giving the same primal equation. At these points,
Newton step. At a chosen target parameter , defineLinearizing the three equality conditions gives the central-path Newton systemEliminating the slack and dual directions yieldsThe barrier Hessian is positive definite. Full column rank of therefore makes positive definite and the reduced solve unique. Use a damped step with and remaining interior; a full Newton step need not do so.
At an exact point with parameter , choosing gives and , the usual predictor towards the next path point. Infinitesimally,
The path definition presupposes interior feasibility and attainment of the barrier problems; strict primal-dual feasibility is a standard sufficient setting. The printed full-rank condition by itself is insufficient. For example , , and give the sole primal feasible point , with no interior slack at all, despite full column rank. Rank guarantees the Newton solve at an interior point, not existence of the central path.
Self-dual cone Created 2026-10-07 Updated 2026-10-14
A self-dual cone equals its dual cone under the specified inner product. The nonnegative orthant, second-order cone and positive semidefinite cone are standard examples. Self-duality of a set alone does not establish primal or dual feasibility.
Self-duality of a second-order cone 2026-10-14
For in the second-order cone, the Cauchy-Schwarz inequality gives . Thus the cone is contained in its dual cone. Conversely, if and , pairing with is negative. If and , use . Every point outside the cone therefore lies outside its dual, proving that it is a self-dual cone.