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Journal articles on the topic '(INFINITE-HORIZON-MODEL)'

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Published: 4 June 2021

Last updated: 1 February 2022

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1

Lykina, Valeriya, Sabine Pickenhain, and Marcus Wagner. "On a resource allocation model with infinite horizon." Applied Mathematics and Computation 204, no. 2 (October 2008): 595–601. http://dx.doi.org/10.1016/j.amc.2008.05.041.

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2

Wakai, Katsutoshi. "An infinite-horizon model of nonmonotone utility smoothing." Economics Letters 116, no. 2 (August 2012): 170–73. http://dx.doi.org/10.1016/j.econlet.2012.02.011.

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3

Griffith, Devin W., Sachin C. Patwardhan, and Lorenz T. Biegler. "Quasi-Infinite Adaptive Horizon Nonlinear Model Predictive Control." IFAC-PapersOnLine 51, no. 18 (2018): 506–11. http://dx.doi.org/10.1016/j.ifacol.2018.09.374.

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4

Sorger, Gerhard. "Strategic saving decisions in the infinite-horizon model." Economic Theory 36, no. 3 (August 24, 2007): 353–77. http://dx.doi.org/10.1007/s00199-007-0273-0.

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5

Lin, Jennifer, Henry C. J. Chao, and Peterson Julian. "Planning Horizon for Production Inventory Models with Production Rate Dependent on Demand and Inventory Level." Journal of Applied Mathematics 2013 (2013): 1–9. http://dx.doi.org/10.1155/2013/961258.

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This paper discusses why the selection of a finite planning horizon is preferable to an infinite one for a replenishment policy of production inventory models. In a production inventory model, the production rate is dependent on both the demand rate and the inventory level. When there is an exponentially decreasing demand, the application of an infinite planning horizon model is not suitable. The emphasis of this paper is threefold. First, while pointing out questionable results from a previous study, we propose a corrected infinite planning horizon inventory model for the first replenishment cycle. Second, while investigating the optimal solution for the minimization problem, we found that the infinite planning horizon should not be applied when dealing with an exponentially decreasing demand. Third, we developed a new production inventory model under a finite planning horizon for practitioners. Numerical examples are provided to support our findings.

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6

Pang, Tao, and Azmat Hussain. "An infinite time horizon portfolio optimization model with delays." Mathematical Control and Related Fields 6, no. 4 (October 2016): 629–51. http://dx.doi.org/10.3934/mcrf.2016018.

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7

Cheung, Ki Ling, and Xue-Ming Yuan. "An infinite horizon inventory model with periodic order commitment." European Journal of Operational Research 146, no. 1 (April 2003): 52–66. http://dx.doi.org/10.1016/s0377-2217(02)00226-6.

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8

Geunes, Joseph P., Ranga V. Ramasesh, and Jack C. Hayya. "Adapting the newsvendor model for infinite-horizon inventory systems." International Journal of Production Economics 72, no. 3 (August 2001): 237–50. http://dx.doi.org/10.1016/s0925-5273(00)00149-3.

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9

Balasko, Yves. "The natural projection approach to the infinite-horizon model." Journal of Mathematical Economics 27, no. 3 (April 1997): 251–65. http://dx.doi.org/10.1016/s0304-4068(96)00784-7.

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10

Nishimura, Kazuo, and Gerhard Sorger. "Non‐linear Dynamics in the Infinite Time Horizon Model." Journal of Economic Surveys 13, no. 5 (December 1999): 619–52. http://dx.doi.org/10.1111/1467-6419.00100.

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11

Bei Hu and A. Linnemann. "Toward infinite-horizon optimality in nonlinear model predictive control." IEEE Transactions on Automatic Control 47, no. 4 (April 2002): 679–82. http://dx.doi.org/10.1109/9.995049.

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12

Marjanovic, Ognjen, and Barry Lennox. "Infinite horizon model predictive control with no terminal constraint." Computers & Chemical Engineering 28, no. 12 (November 2004): 2605–10. http://dx.doi.org/10.1016/j.compchemeng.2004.07.005.

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13

Sethi, S., and M. Taksar. "Infinite-horizon investment consumption model with a nonterminal bankruptcy." Journal of Optimization Theory and Applications 74, no. 2 (August 1992): 333–46. http://dx.doi.org/10.1007/bf00940898.

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14

Greer, William B., and Cornel Sultan. "Infinite horizon model predictive control tracking application to helicopters." Aerospace Science and Technology 98 (March 2020): 105675. http://dx.doi.org/10.1016/j.ast.2019.105675.

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15

Cai, Xin, Shaoyuan Li, Ning Li, and Kang Li. "On Variation of Infinite Horizon Performance of Model Predictive Control with Varying Receding Horizon." IFAC Proceedings Volumes 46, no. 20 (2013): 701–6. http://dx.doi.org/10.3182/20130902-3-cn-3020.00194.

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16

Reynolds, Stanley S. "Capacity Investment, Preemption and Commitment in an Infinite Horizon Model." International Economic Review 28, no. 1 (February 1987): 69. http://dx.doi.org/10.2307/2526860.

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17

Calvo, Guillermo A., and Pablo E. Guidotti. "Optimal Maturity of Nominal Government Debt: An Infinite-Horizon Model." International Economic Review 33, no. 4 (November 1992): 895. http://dx.doi.org/10.2307/2527149.

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18

Papageorgiou, Nikolaos S. "Sensitivity analysis for a continuous time infinite horizon growth model." Applied Mathematics and Computation 60, no. 1 (January 1994): 43–53. http://dx.doi.org/10.1016/0096-3003(94)90205-4.

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19

OMORI, Tatsuya, Toshiyuki SATOH, Naoki SAITO, Norihiko SAGA, and Jun-ya NAGASE. "Evaluation of computational burden for infinite horizon model predictive control." Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec) 2016 (2016): 2P2–04b6. http://dx.doi.org/10.1299/jsmermd.2016.2p2-04b6.

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20

Saez, Emmanuel. "Optimal progressive capital income taxes in the infinite horizon model." Journal of Public Economics 97 (January 2013): 61–74. http://dx.doi.org/10.1016/j.jpubeco.2012.09.002.

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21

Kamalapurkar, Rushikesh, Lindsey Andrews, Patrick Walters, and Warren E. Dixon. "Model-Based Reinforcement Learning for Infinite-Horizon Approximate Optimal Tracking." IEEE Transactions on Neural Networks and Learning Systems 28, no. 3 (March 2017): 753–58. http://dx.doi.org/10.1109/tnnls.2015.2511658.

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22

Chang, Roberto. "Credible Monetary Policy in an Infinite Horizon Model: Recursive Approaches." Journal of Economic Theory 81, no. 2 (August 1998): 431–61. http://dx.doi.org/10.1006/jeth.1998.2395.

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23

Rodrigues, Marco A., and Darci Odloak. "An infinite horizon model predictive control for stable and integrating processes." Computers & Chemical Engineering 27, no. 8-9 (September 2003): 1113–28. http://dx.doi.org/10.1016/s0098-1354(03)00040-1.

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24

Wang, Lisong, Tao Feng, Junhua Song, Zonghao Guo, and Jun Hu. "Model Checking Optimal Infinite-Horizon Control for Probabilistic Gene Regulatory Networks." IEEE Access 6 (2018): 77299–307. http://dx.doi.org/10.1109/access.2018.2881655.

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25

Omell, Benjamin P., and Donald J. Chmielewski. "IGCC Power Plant Dispatch Using Infinite-Horizon Economic Model Predictive Control." Industrial & Engineering Chemistry Research 52, no. 9 (February 2013): 3151–64. http://dx.doi.org/10.1021/ie3008665.

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26

Wilson, John G. "Approximating an Infinite Stage Search Problem with a Finite Horizon Model." Mathematics of Operations Research 14, no. 3 (August 1989): 433–47. http://dx.doi.org/10.1287/moor.14.3.433.

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27

Zhang, Hao. "Solving an Infinite Horizon Adverse Selection Model Through Finite Policy Graphs." Operations Research 60, no. 4 (August 2012): 850–64. http://dx.doi.org/10.1287/opre.1120.1056.

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28

Yu, Shuyou, Marcus Reble, Hong Chen, and Frank Allgöwer. "Inherent robustness properties of quasi-infinite horizon nonlinear model predictive control." Automatica 50, no. 9 (September 2014): 2269–80. http://dx.doi.org/10.1016/j.automatica.2014.07.014.

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29

Rubio, Santiago J., and Alistair Ulph. "An infinite-horizon model of dynamic membership of international environmental agreements." Journal of Environmental Economics and Management 54, no. 3 (November 2007): 296–310. http://dx.doi.org/10.1016/j.jeem.2007.02.004.

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30

Cheung, Ki Ling. "The effects of component commonality in an infinite horizon inventory model." Production Planning & Control 13, no. 3 (January 2002): 326–33. http://dx.doi.org/10.1080/09537280110099637.

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31

Campos, Fco Alberto, José Villar, and Efraim Centeno. "Annualization of Renewable Investment Costs for Finite Horizon Electricity Pricing and Cost Recovery." Sustainability 13, no. 4 (February 12, 2021): 1993. http://dx.doi.org/10.3390/su13041993.

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The increasing penetration of renewable electricity generation is complicating the bidding and estimating processes of electricity prices, partly due to the shift of the overall cost sensitivity from operation (fuel) costs to investment costs. However, cost minimization models for capacity expansion are frequently based on the principle that, for a perfectly adapted system allowing non-served energy, marginal remuneration allows overall operation and investments costs recovery. In addition, these models are usually formulated as finite-horizon problems when they should be theoretically solved for infinite horizons under the assumption of companies’ infinite lifespan, but infinite horizon cannot be dealt with mathematical programming since it requires finite sets. Previous approaches have tried to overcome this drawback with finite horizon models that tend asymptotically to the original infinite ones and, in many cases, the investment costs are annualized based on the plants’ lifespan, sometimes including a cost residual value. This paper proposes a novel approach with a finite horizon that guarantees the investment costs’ recovery. It is also able to obtain the marginal electricity costs of the original infinite horizon model, without the need for residual values or non-served energy. This new approach is especially suited for long-term electricity pricing with investments in renewable assets when non-served demand is banned or when no explicit capacity remuneration mechanisms are considered.

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32

Clifton, Jesse, and Eric Laber. "Q-Learning: Theory and Applications." Annual Review of Statistics and Its Application 7, no. 1 (March 9, 2020): 279–301. http://dx.doi.org/10.1146/annurev-statistics-031219-041220.

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Q-learning, originally an incremental algorithm for estimating an optimal decision strategy in an infinite-horizon decision problem, now refers to a general class of reinforcement learning methods widely used in statistics and artificial intelligence. In the context of personalized medicine, finite-horizon Q-learning is the workhorse for estimating optimal treatment strategies, known as treatment regimes. Infinite-horizon Q-learning is also increasingly relevant in the growing field of mobile health. In computer science, Q-learning methods have achieved remarkable performance in domains such as game-playing and robotics. In this article, we ( a) review the history of Q-learning in computer science and statistics, ( b) formalize finite-horizon Q-learning within the potential outcomes framework and discuss the inferential difficulties for which it is infamous, and ( c) review variants of infinite-horizon Q-learning and the exploration-exploitation problem, which arises in decision problems with a long time horizon. We close by discussing issues arising with the use of Q-learning in practice, including arguments for combining Q-learning with direct-search methods; sample size considerations for sequential, multiple assignment randomized trials; and possibilities for combining Q-learning with model-based methods.

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33

Cai, Qingpeng, Ling Pan, and Pingzhong Tang. "Deterministic Value-Policy Gradients." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (April 3, 2020): 3316–23. http://dx.doi.org/10.1609/aaai.v34i04.5732.

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Reinforcement learning algorithms such as the deep deterministic policy gradient algorithm (DDPG) has been widely used in continuous control tasks. However, the model-free DDPG algorithm suffers from high sample complexity. In this paper we consider the deterministic value gradients to improve the sample efficiency of deep reinforcement learning algorithms. Previous works consider deterministic value gradients with the finite horizon, but it is too myopic compared with infinite horizon. We firstly give a theoretical guarantee of the existence of the value gradients in this infinite setting. Based on this theoretical guarantee, we propose a class of the deterministic value gradient algorithm (DVG) with infinite horizon, and different rollout steps of the analytical gradients by the learned model trade off between the variance of the value gradients and the model bias. Furthermore, to better combine the model-based deterministic value gradient estimators with the model-free deterministic policy gradient estimator, we propose the deterministic value-policy gradient (DVPG) algorithm. We finally conduct extensive experiments comparing DVPG with state-of-the-art methods on several standard continuous control benchmarks. Results demonstrate that DVPG substantially outperforms other baselines.

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34

Beqiraj, Elton, Giovanni Di Bartolomeo, and Carolina Serpieri. "RATIONAL VS. LONG-RUN FORECASTERS: OPTIMAL MONETARY POLICY AND THE ROLE OF INEQUALITY." Macroeconomic Dynamics 23, S1 (August 10, 2017): 9–24. http://dx.doi.org/10.1017/s1365100517000396.

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This paper builds a stylized simple sticky-price New Keynesian model where agents' beliefs are not homogeneous. We assume that agents choose optimal plans while considering forecasts of macroeconomic conditions over an infinite horizon. A fraction of them (boundedly rational agents) use heuristics to forecast macroeconomic variables over an infinite horizon. In our framework, we study optimal policies consistent with a second-order approximation of the policy objective from the consumers' utility function, assuming that the steady state is not distorted.

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35

Glen, J. J. "An Infinite Horizon Mathematical Programming Model of a Multicohort Single Species Fishery." Journal of the Operational Research Society 48, no. 11 (November 1997): 1095. http://dx.doi.org/10.2307/3010305.

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36

Megías, D., J. Serrano, and C. De Prada. "Min-Max Constrained Quasi-Infinite Horizon Model Predictive Control Using Linear Programming." IFAC Proceedings Volumes 33, no. 10 (June 2000): 23–32. http://dx.doi.org/10.1016/s1474-6670(17)38513-0.

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37

Balasko, Yves. "Equilibrium analysis of the infinite horizon model with smooth discounted utility functions." Journal of Economic Dynamics and Control 21, no. 4-5 (May 1997): 783–829. http://dx.doi.org/10.1016/s0165-1889(97)00006-7.

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38

Glen, J. J. "An infinite horizon mathematical programming model of a multicohort single species fishery." Journal of the Operational Research Society 48, no. 11 (November 1997): 1095–104. http://dx.doi.org/10.1057/palgrave.jors.2600477.

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39

Megı́as, D., J. Serrano, and C. de Prada. "Min–max constrained quasi-infinite horizon model predictive control using linear programming." Journal of Process Control 12, no. 4 (June 2002): 495–505. http://dx.doi.org/10.1016/s0959-1524(01)00016-6.

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40

LI, GENDAO, YU XIONG, and ZHONGKAI XIONG. "ROBUST DYNAMIC PRICING OVER INFINITE HORIZON IN THE PRESENCE OF MODEL UNCERTAINTY." Asia-Pacific Journal of Operational Research 26, no. 06 (December 2009): 779–804. http://dx.doi.org/10.1142/s021759590900247x.

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This paper studies a problem of dynamic pricing faced by a retailer with limited inventory, uncertain about the demand rate model, aiming to maximize expected discounted revenue over an infinite time horizon. The retailer doubts his demand model which is generated by historical data and views it as an approximation. Uncertainty in the demand rate model is represented by a notion of generalized relative entropy process, and the robust pricing problem is formulated as a two-player zero-sum stochastic differential game. The pricing policy is obtained through the Hamilton-Jacobi-Isaacs (HJI) equation. The existence and uniqueness of the solution of the HJI equation is shown and a verification theorem is proved to show that the solution of the HJI equation is indeed the value function of the pricing problem. The results are illustrated by an example with exponential nominal demand rate.

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41

Glen, J. J. "An infinite horizon mathematical programming model of a multicohort single species fishery." Journal of the Operational Research Society 48, no. 11 (1997): 1095–104. http://dx.doi.org/10.1038/sj.jors.2600477.

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42

Kiselev, Yu N., and M. V. Orlov. "Analysis of a gas field development model with an infinite planning horizon." Differential Equations 47, no. 11 (November 2011): 1603–11. http://dx.doi.org/10.1134/s0012266111110073.

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43

Chigansky, Pavel, and Ramon van Handel. "Model robustness of finite state nonlinear filtering over the infinite time horizon." Annals of Applied Probability 17, no. 2 (April 2007): 688–715. http://dx.doi.org/10.1214/105051606000000871.

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44

Clark, Stephen A. "Competitive prices for a stochastic input–output model with infinite time horizon." Economic Theory 35, no. 1 (March 13, 2007): 1–17. http://dx.doi.org/10.1007/s00199-007-0225-8.

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45

Ignatov, Zvetan G., and Vladimir K. Kaishev. "On the infinite-horizon probability of (non)ruin for integer-valued claims." Journal of Applied Probability 43, no. 2 (June 2006): 535–51. http://dx.doi.org/10.1239/jap/1152413740.

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We consider a compound Poisson process whose jumps are modelled as a sequence of positive, integer-valued, dependent random variables, W1,W2,…, viewed as insurance claim amounts. The number of points up to time t of the stationary Poisson process which models the claim arrivals is assumed to be independent of W1,W2,…. The premium income to the insurance company is represented by a nondecreasing, nonnegative, real-valued function h(t) on [0,∞) such that limt→∞h(t) = ∞. The function h(t) is interpreted as an upper boundary. The probability that the trajectory of such a compound Poisson process will not cross the upper boundary in infinite time is known as the infinite-horizon nonruin probability. Our main result in this paper is an explicit expression for the probability of infinite-horizon nonruin, assuming that certain conditions on the premium-income function, h(t), and the joint distribution of the claim amount random variables, W1,W2,…, hold. We have also considered the classical ruin probability model, in which W1,W2,… are assumed to be independent, identically distributed random variables and we let h(t)=u + ct. For this model we give a formula for the nonruin probability which is a special case of our main result. This formula is shown to coincide with the infinite-horizon nonruin probability formulae of Picard and Lefèvre (2001), Gerber (1988), (1989), and Shiu (1987), (1989).

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46

Lee, Ronald, and Michael Anderson. "Stochastic Infinite Horizon Forecasts for US Social Security Finances." National Institute Economic Review 194 (October 2005): 82–93. http://dx.doi.org/10.1177/0027950105061498.

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Even over a 75-year horizon, forecasts of PAYGO pension finances are misleadingly optimistic. Infinite horizon forecasts are necessary, but are they possible? We build on earlier stochastic forecasts of the US Social Security trust fund which model key demographic and economic variables as historical time series, and use the fitted models to generate Monte Carlo simulations of future fund performance. Using a 500-year stochastic projection, effectively infinite with discounting, we find a fund balance of −5.15 per cent of payroll, compared to the −3.5 per cent of the 2004 Trustees‘ Report, probably reflecting different mortality projections. Our 95 per cent probability bounds are −10.5 and −1.3 per cent. Such forecasts, which reflect only ‘routine’ uncertainty, have many problems but nonetheless seem worthwhile.

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47

Chatterjee, Kalyan, and Larry Samuelson. "Bargaining with Two-sided Incomplete Information: An Infinite Horizon Model with Alternating Offers." Review of Economic Studies 54, no. 2 (April 1987): 175. http://dx.doi.org/10.2307/2297510.

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48

Bloemen, H. H. J., and T. J. J. van den Boom. "Constrained linear model-based predictive control with an infinite control and prediction horizon." IFAC Proceedings Volumes 32, no. 2 (July 1999): 1243–48. http://dx.doi.org/10.1016/s1474-6670(17)56210-2.

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49

Baocang Ding. "Comments on "Constrained Infinite-Horizon Model Predictive Control for Fuzzy-Discrete-Time Systems." IEEE Transactions on Fuzzy Systems 19, no. 3 (June 2011): 598–600. http://dx.doi.org/10.1109/tfuzz.2011.2126580.

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50

Lamond, Bernard F., and Pascal Lang. "Lower bounding aggregation and direct computation for an infinite horizon one-reservoir model." European Journal of Operational Research 95, no. 2 (December 1996): 404–10. http://dx.doi.org/10.1016/0377-2217(96)00262-7.

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