Abstract State Machines 1988-1998: Commented ASM Bibliography

An annotated bibliography of papers which deal with or use Abstract State Machines (ASMs), as of January 1998.Comment: Also maintained as a BibTeX file at http://www.eecs.umich.edu/gasm

Efficient option pricing with transaction costs

A fast numerical algorithm is developed to price European options with proportional transaction costs using the utility-maximization framework of Davis (1997). This approach allows option prices to be computed by solving the investor’s basic portfolio selection problem without insertion of the option payoff into the terminal value function. The properties of the value function can then be used to drastically reduce the number of operations needed to locate the boundaries of the no-transaction region, which leads to very efficient option valuation. The optimization problem is solved numerically for the case of exponential utility, and comparisons with approximately replicating strategies reveal tight bounds for option prices even as transaction costs become large. The computational technique involves a discrete-time Markov chain approximation to a continuous-time singular stochastic optimal control problem. A general definition of an option hedging strategy in this framework is developed. This involves calculating the perturbation to the optimal portfolio strategy when an option trade is executed

An Optimization Model for Single-Warehouse Multi-Agents Distribution Network Problems under Varying of Transportation Facilities: A Case Study

The transportation cost of goods is the highest day-to-day operational cost associated with the food industry sector. A company may be able to reduce logistics cost and simultaneously improve service level by optimizing of distribution network. In reality, a company faces problems considering capacitated transportation facilities and time constraint of delivery. In this paper, we develop a new model of order fulfillment physical distribution to minimize transportation cost under limited of transportation facilities. The first step is defined problem description. After that, we formulate a integer linear programming model for the single-warehouse, multiple-agents considering varying of transportation facilities in multi-period shipment planning. We analyze problems faced by company when should decide policy of distribution due to varying of transportation facilities in volume, type of vehicle, delivery cost, lead time and ownership of facilities. We assumed transportation costs are modeled with a linear term in the objective function. Then, we solve the model with Microsoft Excel Solver 8.0 Version. Finally, we analyze the results with considering amount of transportation facilities, volume usage and total transportation cost. Keywords: physical distribution, shipment planning, integer linear programming, transportation cost, transportation facilities

Automatic differentiation in machine learning: a survey

Derivatives, mostly in the form of gradients and Hessians, are ubiquitous in machine learning. Automatic differentiation (AD), also called algorithmic differentiation or simply "autodiff", is a family of techniques similar to but more general than backpropagation for efficiently and accurately evaluating derivatives of numeric functions expressed as computer programs. AD is a small but established field with applications in areas including computational fluid dynamics, atmospheric sciences, and engineering design optimization. Until very recently, the fields of machine learning and AD have largely been unaware of each other and, in some cases, have independently discovered each other's results. Despite its relevance, general-purpose AD has been missing from the machine learning toolbox, a situation slowly changing with its ongoing adoption under the names "dynamic computational graphs" and "differentiable programming". We survey the intersection of AD and machine learning, cover applications where AD has direct relevance, and address the main implementation techniques. By precisely defining the main differentiation techniques and their interrelationships, we aim to bring clarity to the usage of the terms "autodiff", "automatic differentiation", and "symbolic differentiation" as these are encountered more and more in machine learning settings.Comment: 43 pages, 5 figure