In this paper, we study the Robust optimization for sequence Networked submodular maximization (RoseNets) problem. We interweave robust with networked maximization. The elements are connected by a directed acyclic graph and objective function is not on but edges in graph. Under such scenario, impact of removing an element from depends both its position network. This makes existing algorithms in...

Many AI/Machine learning problems require adaptively selecting a sequence of items, each selected item might provide some feedback that is valuable for making better selections in the future, with goal maximizing an adaptive submodular function. Most existing studies this field focus on either monotone case or non-monotone case. Specifically, if utility function and submodular, Golovin Krause (...

Abstract This paper studies arbitrage-free financial markets with bid-ask spreads whose super-hedging prices are submodular. The submodular assumption on the price, or supermodularity usually assumed utility functions, is formal expression of perfect complementarity, which dates back to Fisher, Pareto, and Edgeworth, according Samuelson (J Econ Lit 12:1255–1289, 1974). Our main contribution pro...

Adaptive submodular maximization has been extensively studied in the literature. However, most of existing studies this field focus on pool-based setting, where one is allowed to pick items any order, and there have few for stream-based setting arrive an arbitrary order must immediately decide whether select item or not upon its arrival. In paper, we introduce a new class utility functions, sem...

We consider the problem of maximizing a non-negative monotone submodular function subject to knapsack constraint, which is also known as Budgeted Submodular Maximization (BSM) problem. Sviridenko (Operat Res Lett 32:41–43, 2004) showed that by guessing 3 appropriate elements an optimal solution, and then executing greedy algorithm, one can obtain approximation ratio $$\alpha =1-{1}/{e} \approx ...