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Algorithmic Game Theory and Internet Computing

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## Algorithmic Game Theory and Internet Computing

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**Primal-Dual Algorithms for**Rational Convex Programs I Algorithmic Game Theoryand Internet Computing Vijay V. Vazirani Georgia Tech**Rational convex program**• A nonlinear convex program that always has a rational solution, using polynomially many bits, if all parameters are rational.**Rational convex program**• A nonlinear convex program that always has a rational solution, using polynomially many bits, if all parameters are rational. • i.e., it “behaves” like an LP!**Central Tenet withinMathematical Economics**• Markets should operate at equilibrium i.e., prices s.t. Parity between supply and demand**Easy if only one good!**Domarketsevenadmitequilibriumprices?**What if there are multiple goods and multiple buyers with**diverse desires and different buying power? Domarketsevenadmitequilibriumprices?**Irving Fisher, 1891**• Defined a fundamental market model • Special case of Walras’ model**amount ofj**Concave utility function (for good j) utility**Several buyers with different utility functions and**moneys.Equilibrium prices**Several buyers with different utility functions and**moneys.Find equilibrium prices!**Linear utility function**utility of i amount ofj**Linear Fisher Market**• Assume: • Buyer i’s total utility, • mi: money of buyer i. • One unit of each good j. • n buyers and g goods. • Findequilibrium prices!**Eisenberg-Gale Program, 1959**prices pj**Why remarkable?**• Equilibrium simultaneously optimizes for all agents. • How is this done via a single objective function?**Eisenberg-Gale Program**Theorem: Optimal soln. gives equilibrium allocations, dual gives prices. Equilibrium utilities and prices are unique. Show: Each buyer spends all her money & buys an optimal bundle of goods. All goods fully sold.**Proof of rationality**• Guess positive allocation variables (say k). • Substitute 1/pjby a new variable, say qj • LP: with (k + g) unknowns. Constraints: • Inequalities: KKT Conds. (3) & non-negativity. • Equations: KKT Conds. (2) & (4).**Combinatorial Algorithm for Linear Case of Fisher’s Model**• Devanur, Papadimitriou, Saberi & V., 2002 By extending the primal-dual paradigm to the setting of convex programs & KKT conditions**Combinatorial algorithms**Yield deep structural insights. Preferable for applications.**Auction for Google’s TV ads**N. Nisan et. al, 2009: • Used market equilibrium based approach. • Combinatorial algorithms for linear case provided “inspiration”.**An easier question**• Given prices p, are they equilibrium prices? • If so, find equilibrium allocations.**An easier question**• Given prices p, are they equilibrium prices? • If so, find equilibrium allocations. • Equilibrium prices are unique!**Bang-per-buck**• At prices p, buyer i’s most desirable goods, Si = • Any goods from Siworth m(i) constitute i’s optimal bundle**For each buyer, most desirable goods, i.e.**m(1) p(1) m(2) p(2) m(3) p(3) m(4) p(4)**Network N(p)**p(1) m(1) p(2) m(2) t s p(3) m(3) m(4) p(4) infinite capacities**Max flow in N(p)**p(1) m(1) p(2) m(2) p(3) m(3) m(4) p(4) p: equilibrium prices iff both cuts saturated**Idea of algorithm**• “primal” variables: allocations • “dual” variables: prices of goods • Approach equilibrium prices from below: • start with very low prices; buyers have surplus money • iteratively keep raising prices and decreasing surplus**An important consideration**• The price of a good never exceeds its equilibrium price • Invariant: s is a min-cut**Invariant: s is a min-cut in N(p)**p(1) m(1) p(2) m(2) s p(3) m(3) m(4) p(4) p: low prices**AllocationsPrices**Idea of algorithm • Iterations: execute primal & dual improvements**Fundamental difference betweenLP’s and convex programs**• Complementary slackness conditions: involve primal or dual variables, not both. • KKT conditions: involve primal and dual variables simultaneously.**Primal-dual algorithms so far(i.e., LP-based)**• Raise dual variables greedily. (Lot of effort spent on designing more sophisticated dual processes.)**Primal-dual algorithms so far**• Raise dual variables greedily. (Lot of effort spent on designing more sophisticated dual processes.) • Only exception: Edmonds, 1965: algorithm for max weight matching.**Primal-dual algorithms so far**• Raise dual variables greedily. (Lot of effort spent on designing more sophisticated dual processes.) • Only exception: Edmonds, 1965: algorithm for max weight matching. • Otherwise primal objects go tight and loose. Difficult to account for these reversals -- in the running time.**Our algorithm**• Dual variables (prices) are raised greedily • Yet, primal objects go tight and loose • Because of enhanced KKT conditions