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  • 1
    ISSN: 1572-9338
    Keywords: Optimal control ; Markov chains ; partial observability ; average cost ; optimality equation ; structured optimal policies
    Source: Springer Online Journal Archives 1860-2000
    Topics: Mathematics , Economics
    Notes: Abstract We consider partially observable Markov decision processes with finite or countably infinite (core) state and observation spaces and finite action set. Following a standard approach, an equivalent completely observed problem is formulated, with the same finite action set but with anuncountable state space, namely the space of probability distributions on the original core state space. By developing a suitable theoretical framework, it is shown that some characteristics induced in the original problem due to the countability of the spaces involved are reflected onto the equivalent problem. Sufficient conditions are then derived for solutions to the average cost optimality equation to exist. We illustrate these results in the context of machine replacement problems. Structural properties for average cost optimal policies are obtained for a two state replacement problem; these are similar to results available for discount optimal policies. The set of assumptions used compares favorably to others currently available.
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  • 2
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    Springer
    Journal of theoretical probability 12 (1999), S. 255-270 
    ISSN: 1572-9230
    Keywords: Optimal control ; stochastic differential equations ; convergence in law ; unbounded control set ; suboptimal control
    Source: Springer Online Journal Archives 1860-2000
    Topics: Mathematics
    Notes: Abstract We describe a change of time technique for stochastic control problems with unbounded control set. We demonstrate the technique on a class of maximization problems that do not have optimal controls. Given such a problem, we introduce an extended problem which has the same value function as the original problem and for which there exist optimal controls that are expressible in simple terms. This device yields a natural sequence of suboptimal controls for the original problem. By this we mean a sequence of controls for which the payoff functions approach the value function.
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  • 3
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    Springer
    Annals of operations research 37 (1992), S. 375-401 
    ISSN: 1572-9338
    Keywords: Optimal control ; stochastic control ; dynamic systems ; nonlinear systems ; control algorithm ; optimal economic policies
    Source: Springer Online Journal Archives 1860-2000
    Topics: Mathematics , Economics
    Notes: Abstract In this paper we describe the algorithm OPTCON which has been developed for the optimal control of nonlinear stochastic models. It can be applied to obtain approximate numerical solutions of control problems where the objective function is quadratic and the dynamic system is nonlinear. In addition to the usual additive uncertainty, some or all of the parameters of the model may be stochastic variables. The optimal values of the control variables are computed in an iterative fashion: First, the time-invariant nonlinear system is linearized around a reference path and approximated by a time-varying linear system. Second, this new problem is solved by applying Bellman's principle of optimality. The resulting feedback equations are used to project expected optimal state and control variables. These projections then serve as a new reference path, and the two steps are repeated until convergence is reached. The algorithm has been implemented in the statistical programming system GAUSS. We derive some mathematical results needed for the algorithm and give an overview of the structure of OPTCON. Moreover, we report on some tentative applications of OPTCON to two small macroeconometric models for Austria.
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  • 4
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    Springer
    Annals of operations research 54 (1994), S. 237-262 
    ISSN: 1572-9338
    Keywords: Optimal control ; differential games ; environmental policy ; JEL C61 ; C73 ; Q28
    Source: Springer Online Journal Archives 1860-2000
    Topics: Mathematics , Economics
    Notes: Abstract This paper provides some differential game models of natural resource exploitation when environmental pollution takes place. The “classical” approach to determine optimal harvest rates of renewable resources utilizes Optimal Control models, i.e. there is either a monopolistic market structure or there is pure competition. In case of pollution, however, all agents can be put together, forming the groups of the resource harvesters on one side and of polluters on the other side. So differential games can be used to analyze environmental problems. The models introduced in this paper are put together in order to showdifferent problems that can all be analyzed using differential games.
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  • 5
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    Springer
    Annals of operations research 58 (1995), S. 379-402 
    ISSN: 1572-9338
    Keywords: Optimal control ; stochastic control ; dynamic systems ; economics ; public-sector applications ; optimization ; budgetary policies ; monetary policy
    Source: Springer Online Journal Archives 1860-2000
    Topics: Mathematics , Economics
    Notes: Abstract In this paper, we determine optimal budgetary and monetary policies for Austria using a small macroeconometric model. We use a Keynesian model of the Austrian economy, called FINPOL1, estimated by ordinary least squares, which relates the main objective variables of Austrian economic policies, such as the growth rate of real gross domestic product, the rate of unemployment, the rate of inflation, the balance of payments, and the ratio of the federal budget deficit to GDP, to fiscal and monetary policy instruments, namely expenditures and revenues of the federal budget and money supply. Optimal fiscal and monetary policies are calculated for the model under a quadratic objective function using the algorithm OPTCON for the optimum control of nonlinear stochastic dynamic systems. Several control experiments are performed in order to assess the influence of different kinds of uncertainty on optimal budgetary and monetary policies. Apart from deterministic optimization runs, different assumptions about parameter uncertainties are introduced; the results of these different stochastic optimum control experiments are compared and interpreted.
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  • 6
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    Springer
    Annals of operations research 54 (1994), S. 217-235 
    ISSN: 1572-9338
    Keywords: Optimal control ; growth paths ; exhaustible resources ; JEL C61 ; O41 ; Q23
    Source: Springer Online Journal Archives 1860-2000
    Topics: Mathematics , Economics
    Notes: Abstract This paper focuses on the issue of optimal pollution control when either pollution itself is irreversible or when some characteristic of the environmental resource is irreversibly destroyed in the course of growing pollution. It is shown that exhausting the assimilative capacity through too heavy pollution is never optimal unless the rate of social time preference is sufficiently high. The paper also investigates the case that decisions about irreversible developments have to be made under uncertainty today when the decision maker faces the prospect of better information about the irreversible damage at some future point in time. A non-negative quasi-option value is shown to exist as in the Arrow-Fisher-Henry model that relates to natural resource deletion by projects of industrial development.
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  • 7
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    Springer
    Applied mathematics & optimization 32 (1995), S. 73-97 
    ISSN: 1432-0606
    Keywords: Optimal control ; State constraint ; Volterra integral equation ; Necessary conditions ; Controllability ; Sensitivity ; Value function ; Proximal normal analysis ; Proximal analysis ; 49J22 ; 49K22 ; 93B05 ; 93B06 ; 93B35
    Source: Springer Online Journal Archives 1860-2000
    Topics: Mathematics
    Notes: Abstract Infinite-dimensional perturbations in all constraints of an optimal control problem governed by a Volterra integral equation with the presence of a state constraint are considered. These perturbations give rise to a value function, whose analysis through the proximal normal technique provides sensitivity, controllability, and even necessary conditions for the basic problem. Actually all information about the value function is contained in Clarke's normal cone of its epigraph, which can be characterized by the proximal normal formula.
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  • 8
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    Applied mathematics & optimization 33 (1996), S. 1-33 
    ISSN: 1432-0606
    Keywords: Optimal control ; Value function ; Analytic semigroups ; Semiconcave functions ; Distributed parameter systems ; 49K20 ; 49L20 ; 47B44 ; 49J52
    Source: Springer Online Journal Archives 1860-2000
    Topics: Mathematics
    Notes: Abstract In this paper we continue to study properties of the value function and of optimal solutions of a semilinear Mayer problem in infinite dimensions. Applications concern systems governed by a state equation of parabolic type. In particular, the issues of the joint Lipschitz continuity and semiconcavity of the value function are treated in order to investigate the differentiability of the value function along optimal trajectories.
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  • 9
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    Algorithmica 15 (1996), S. 17-49 
    ISSN: 1432-0541
    Keywords: Scheduling ; Multiprocessor scheduling ; Parallel algorithms ; NP-completeness ; Theoretical computer science ; Operations research ; Optimal control
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Mathematics
    Notes: Abstract In this paper we present several new results in the theory of homogeneous multiprocessor scheduling. We start with some assumptions about the behavior of tasks, with associated precedence constraints, as processor power is applied. We assume that as more processors are applied to a task, the time taken to compute it decreases, yielding some speedup. Because of communication, synchronization, and task scheduling overhead, this speedup increases less than linearly with the number of processors applied. We also assume that the number of processors which can be assigned to a task is a continuous variable, with a view to exploiting continuous mathematics. The optimal scheduling problem is to determine the number of processors assigned to each task, and task sequencing, to minimize the finishing time. These assumptions allow us to recast the optimal scheduling problem in a form which can be addressed by optimal control theory. Various theorems can be proven which characterize the optimal scheduling solution. Most importantly, for the special case where the speedup function of each task isp α , wherep is the amount of processing power applied to the task, we can directly solve our equations for the optimal solution. In this case, for task graphs formed from parallel and series connections, the solution can be derived by inspection. The solution can also be shown to be shortest path from the initial to the final state, as measured by anl 1/α distance metric, subject to obstacle constraints imposed by the precedence constraints.
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  • 10
    Electronic Resource
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    Algorithmica 14 (1995), S. 443-479 
    ISSN: 1432-0541
    Keywords: Robot motion planning ; Optimal control ; Polynomial-timeɛ-approximation algorithm ; Time-optimal trajectory ; Shortest path ; Kinodynamics ; Polyhedral obstacles
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Mathematics
    Notes: Abstract We consider the following problem: given a robot system, find a minimal-time trajectory that goes from a start state to a goal state while avoiding obstacles by a speed-dependent safety margin and respecting dynamics bounds. In [1] we developed a provably good approximation algorithm for the minimum-time trajectory problem for a robot system with decoupled dynamics bounds (e.g., a point robot in ℝ3). This algorithm differs from previous work in three ways. It is possible (1) to bound the goodness of the approximation by an error termɛ; (2) to bound the computational complexity of our algorithm polynomially; and (3) to express the complexity as a polynomial function of the error term. Hence, given the geometric obstacles, dynamics bounds, and the error termɛ, the algorithm returns a solution that isɛ-close to optimal and requires only a polynomial (in (1/ɛ)) amount of time. We extend the results of [1] in two ways. First, we modify it to halve the exponent in the polynomial bounds from 6d to 3d, so that the new algorithm isO(c d N 1/ɛ)3d ), whereN is the geometric complexity of the obstacles andc is a robot-dependent constant. Second, the new algorithm finds a trajectory that matches the optimal in time with anɛ factor sacrificed in the obstacle-avoidance safety margin. Similar results hold for polyhedral Cartesian manipulators in polyhedral environments. The new results indicate that an implementation of the algorithm could be reasonable, and a preliminary implementation has been done for the planar case.
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