Solving Covering / Packing LPs via Saddle-point Problems

Antares Chen University of Chicago


Packing linear programs $$\max \{ \langle c, x \rangle : Ax \leq b, \, x \geq 0 \}$$ and their dual covering linear programs $$\min \{ \langle b, y \rangle : A^{\top}y \geq c, \, y \geq 0 \}$$ are special cases of linear programming where the variables, coefficients, and constraints are non-negative. In this talk, we present some initial calculations that derive an algorithm for approximately solving packing and covering LPs via minimizing the duality gap – the difference between the packing and covering objectives. An interesting aspect of our calculations is that the duality gap comes from reformulating the LP as a 2-v-2 game captured by a saddle point problem. Our algorithm emerges by requiring players of the game to adopt a specific strategy. However, other algorithms with potentially faster convergence rates may emerge by requiring players of the 2-v-2 game to adopt other strategies. Based on joint work with Lorenzo Orecchia, and Erasmo Tani.

Feb 16, 2022 12:30 PM — 1:30 PM
Theory Lunch
JCL 390

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