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Decentralized bilevel optimization Optim. Lett. (IF 1.6) Pub Date : 2024-03-26 Xuxing Chen, Minhui Huang, Shiqian Ma
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On the implementation of ADMM with dynamically configurable parameter for the separable $$\ell _{1}/\ell _{2}$$ minimization Optim. Lett. (IF 1.6) Pub Date : 2024-03-26 Jun Wang, Qiang Ma
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The projected splitting iterative methods based on tensor splitting and its majorization matrix splitting for the tensor complementarity problem Optim. Lett. (IF 1.6) Pub Date : 2024-03-24 Mengxiao Fan, Jicheng Li
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Subdifferentials of convex matrix-valued functions Optim. Lett. (IF 1.6) Pub Date : 2024-03-21
Abstract Subdifferentials (in the sense of convex analysis) of matrix-valued functions defined on \(\mathbb {R}^d\) that are convex with respect to the Löwner partial order can have a complicated structure and might be very difficult to compute even in simple cases. The aim of this paper is to study subdifferential calculus for such functions and properties of their subdifferentials. We show that many
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Learning to project in a criterion space search algorithm: an application to multi-objective binary linear programming Optim. Lett. (IF 1.6) Pub Date : 2024-03-18 Alvaro Sierra-Altamiranda, Hadi Charkhgard, Iman Dayarian, Ali Eshragh, Sorna Javadi
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Convergence analysis of the DFP algorithm for unconstrained optimization problems on Riemannian manifolds Optim. Lett. (IF 1.6) Pub Date : 2024-03-12 Xiao-bo Li, Kai Tu, Jian Lu
In this paper, we propose the DFP algorithm with inexact line search for unconstrained optimization problems on Riemannian manifolds. Under some reasonable conditions, the global convergence result is established and the superlinear local convergence rate of the DFP algorithm is proved on Riemannian manifolds. The preliminary computational experiment is also reported to illustrate the effectiveness
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Stochastic single-machine scheduling with workload-dependent maintenance activities Optim. Lett. (IF 1.6) Pub Date : 2024-03-11 Manzhan Gu, Weitao Yang, Peihai Liu
This paper studies the stochastic single-machine scheduling problem with workload-dependent maintenance activities, in which the processing times of all jobs are independently subject to a common discrete distribution, and the aim is to find the optimal policy so as to minimize the expected total discounted holding cost. Based on the definition of Markov process, for each of the two cases with the
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On the relation between affinely adjustable robust linear complementarity and mixed-integer linear feasibility problems Optim. Lett. (IF 1.6) Pub Date : 2024-03-05
Abstract We consider adjustable robust linear complementarity problems and extend the results of Biefel et al. (SIAM J Optim 32:152–172, 2022) towards convex and compact uncertainty sets. Moreover, for the case of polyhedral uncertainty sets, we prove that computing an adjustable robust solution of a given linear complementarity problem is equivalent to solving a properly chosen mixed-integer linear
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A distributional Farkas’ lemma and moment optimization problems with no-gap dual semi-definite programs Optim. Lett. (IF 1.6) Pub Date : 2024-03-04 Queenie Yingkun Huang, Vaithilingam Jeyakumar
We present a generalized Farkas’ Lemma for an inequality system involving distributions. This lemma establishes an equivalence between an infinite-dimensional system of moment inequalities and a semi-definite system, assuming that the support for the distributions is a spectrahedron. To the best of our knowledge, it is the first extension of Farkas’ Lemma to the distributional paradigm. Applying the
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A new approach to the multiple obnoxious facility location problem based on combinatorial and continuous tools Optim. Lett. (IF 1.6) Pub Date : 2024-03-03 M. Locatelli
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Integrated optimization of design and production process with personalization level of products Optim. Lett. (IF 1.6) Pub Date : 2024-02-27 Ba-Yi Cheng, Jie Duan, Xin-Yan Shi, Mi Zhou
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Hausdorff continuity of solution maps to equilibrium problems via the oriented distance function Optim. Lett. (IF 1.6) Pub Date : 2024-02-27 Lam Quoc Anh, Nguyen Huu Danh, Pham Thanh Duoc
This paper aims to study the stability in the sense of Hausdorff continuity of solution maps to equilibrium problems without assuming the solid condition of ordered cones. We first propose a generalized concavity of set-valued maps and discuss its relation with the existing concepts. Then, by using the above property and the continuity of the objective function, sufficient conditions for the Hausdorff
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Exact QR factorizations of rectangular matrices Optim. Lett. (IF 1.6) Pub Date : 2024-02-22 Christopher Lourenco, Erick Moreno-Centeno
QR factorization is a key tool in mathematics, computer science, operations research, and engineering. This paper presents the roundoff-error-free (REF) QR factorization framework comprising integer-preserving versions of the standard and the thin QR factorizations and associated algorithms to compute them. Specifically, the standard REF QR factorization factors a given matrix \(A\in {\mathbb {Z}}^{m\times
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Local convergence analysis of an inexact trust-region method for nonsmooth optimization Optim. Lett. (IF 1.6) Pub Date : 2024-02-21 Robert J. Baraldi, Drew P. Kouri
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Optimality conditions for robust weakly efficient solutions in uncertain optimization Optim. Lett. (IF 1.6) Pub Date : 2024-02-13 Yuwen Zhai, Qilin Wang, Tian Tang, Maoyuan Lv
In this paper, we find the flimsily robust weakly efficient solution to the uncertain vector optimization problem by means of the weighted sum scalarization method and strictly robust counterpart. In addition, we introduce a higher-order weak upper inner Studniarski epiderivative of set-valued maps, and obtain two properties of the new notion under the assumption of the star-shaped set. Finally, by
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Characterization of unique solvability of absolute value equations: an overview, extensions, and future directions Optim. Lett. (IF 1.6) Pub Date : 2024-02-09 Shubham Kumar, Deepmala, Milan Hladík, Hossein Moosaei
This paper provides an overview of the necessary and sufficient conditions for guaranteeing the unique solvability of absolute value equations. In addition to discussing the basic form of these equations, we also address several generalizations, including generalized absolute value equations and matrix absolute value equations. Our survey encompasses known results as well as novel characterizations
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A fast primal-dual algorithm via dynamical system with variable mass for linearly constrained convex optimization Optim. Lett. (IF 1.6) Pub Date : 2024-01-28 Ziyi Jiang, Dan Wang, Xinwei Liu
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Constrained many-to-many point matching in two dimensions Optim. Lett. (IF 1.6) Pub Date : 2024-01-26 L. E. Caraballo, R. A. Castro, J. M. Díaz-Báñez, M. A. Heredia, J. Urrutia, I. Ventura, F. J. Zaragoza
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Differentially private k-center problems Optim. Lett. (IF 1.6) Pub Date : 2024-01-25 Fan Yuan, Dachuan Xu, Donglei Du, Min Li
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Duality in the problems of optimal control described by Darboux-type differential inclusions Optim. Lett. (IF 1.6) Pub Date : 2024-01-25 Sevilay Demir Sağlam
This paper is devoted to the optimization of the Mayer problem with hyperbolic differential inclusions of the Darboux type and duality. We use the discrete approximation method to get sufficient conditions of optimality for the convex problem given by Darboux differential inclusions and the polyhedral problem for a hyperbolic differential inclusion with state constraint. We formulate the adjoint inclusions
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Determining optimal channel partition for 2:4 fine grained structured sparsity Optim. Lett. (IF 1.6) Pub Date : 2024-01-11
Abstract Deep Neural Networks (DNNs) have demonstrated tremendous success in many applications, but incur high computational burden on the inference side. The 2:4 sparsity pruning method has recently been developed to effectively compress and accelerate DNNs with little to no loss in performance. The method comprises a training phase followed by a pruning step where 2 out of 4 consecutive weights are
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Representation of positive polynomials on a generalized strip and its application to polynomial optimization Optim. Lett. (IF 1.6) Pub Date : 2024-01-11
Abstract We study the representation of nonnegative polynomials in two variables on a certain class of unbounded closed basic semi-algebraic sets (which are called generalized strips). This class includes the strip \([a,b] \times {\mathbb {R}}\) which was studied by Marshall in (Proc Am Math Soc 138(5):1559–1567, 2010). A denominator-free Nichtnegativstellensätz holds true on a generalized strip when
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A reduced Jacobian method with full convergence property Optim. Lett. (IF 1.6) Pub Date : 2024-01-07 M. El Maghri, Y. Elboulqe
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Single machine scheduling to minimize maximum earliness/tardiness cost with job rejection Optim. Lett. (IF 1.6) Pub Date : 2024-01-05 Matan Atsmony, Gur Mosheiov
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Random-reshuffled SARAH does not need full gradient computations Optim. Lett. (IF 1.6) Pub Date : 2023-12-11 Aleksandr Beznosikov, Martin Takáč
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On constraint qualifications and optimality conditions for robust optimization problems through pseudo-differential Optim. Lett. (IF 1.6) Pub Date : 2023-12-11 Mansoureh Alavi Hejazi, Nooshin Movahedian
In this paper, a nonsmooth nonconvex robust optimization problem is considered. Using the idea of pseudo-differential, nonsmooth versions of the Robinson, Mangasarian–Fromovitz and Abadie constraint qualifications are introduced and their relations with the existence of a local error bound are investigated. Based on the pseudo-differential notion, new necessary optimality conditions are derived under
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A self adaptive inertial algorithm for solving variational inequalities over the solution set of the split variational inequality problem Optim. Lett. (IF 1.6) Pub Date : 2023-12-07 Nguyen Thi Thu Thuy, Tran Thanh Tung
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Randomized Lagrangian stochastic approximation for large-scale constrained stochastic Nash games Optim. Lett. (IF 1.6) Pub Date : 2023-12-04 Zeinab Alizadeh, Afrooz Jalilzadeh, Farzad Yousefian
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An improved model for estimating optimal VRP solution values Optim. Lett. (IF 1.6) Pub Date : 2023-12-06 Shuhan Kou, Bruce Golden, Luca Bertazzi
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Two-machine job shop scheduling with optional job rejection Optim. Lett. (IF 1.6) Pub Date : 2023-12-06 Ren-Xia Chen, Shi-Sheng Li
We investigate a two-machine job shop scheduling problem with optional job rejection. The target is to look for a feasible schedule for the set of accepted jobs so that the sum of the makespan of the accepted jobs and the total penalty of the rejected jobs is minimized. We propose an exact pseudo-polynomial dynamic programming algorithm, a greedy \(\frac{\sqrt{5}+1}{2}\)-approximation algorithm, an
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Mixed lattice structures and cone projections Optim. Lett. (IF 1.6) Pub Date : 2023-12-02 Jani Jokela
Problems related to projections on closed convex cones are frequently encountered in optimization theory and related fields. To study these problems, various unifying ideas have been introduced, including asymmetric vector-valued norms and certain generalized lattice-like operations. We propose a new perspective on these studies by describing how the problem of cone projection can be formulated using
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A polytime preprocess algorithm for the maximum independent set problem Optim. Lett. (IF 1.6) Pub Date : 2023-11-24 Samuel Kroger, Hamidreza Validi, Illya V. Hicks
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A criterion for Q-tensors Optim. Lett. (IF 1.6) Pub Date : 2023-11-14 Sonali Sharma, K. Palpandi
A tensor \({\mathcal {A}}\) of order m and dimension n is called a \({\mathrm Q}\)-tensor if the tensor complementarity problem has a solution for all \(\mathbf{{q}} \in {{\mathbb {R}}^n}\). This means that for every vector \(\mathbf{{q}}\), there exists a vector \({\mathbf{{u}}}\) such that \({\mathbf{{u}}} \ge \textbf{0},{\textbf{w}} = {\mathcal {A}}{\mathbf{{u}}}^{m-1}+\mathbf{{q}} \ge \textbf{0}
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Improved approximation algorithm for the parallel-machine customer order scheduling with delivery time and submodular rejection penalties Optim. Lett. (IF 1.6) Pub Date : 2023-10-30 Bo Hou, Hongye Zheng, Wen Liu, Weili Wu, Ding-Zhu Du, Suogang Gao
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On the exact solution of the multi-depot open vehicle routing problem Optim. Lett. (IF 1.6) Pub Date : 2023-10-23 Vinícius Carvalho Soares, Marcos Roboredo
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Plants’ competition under autotoxicity effect: an evolutionary game Optim. Lett. (IF 1.6) Pub Date : 2023-10-19 Nikolaos Karagiannis-Axypolitidis, Fabrizio Panebianco, Giuliano Bonanomi, Francesco Giannino
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Single machine scheduling with maintenance and position-based job eligibility constraints for battery manufacturing Optim. Lett. (IF 1.6) Pub Date : 2023-10-16 Sang-Wook Lee, Hyun-Jung Kim
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The truck–drone routing optimization problem: mathematical model and a VNS approach Optim. Lett. (IF 1.6) Pub Date : 2023-10-10 Malick Ndiaye, Ahmed Osman, Said Salhi, Batool Madani
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Buffered and Reduced Multidimensional Distribution Functions and Their Application in Optimization Optim. Lett. (IF 1.6) Pub Date : 2023-10-09 Bogdan Grechuk, Michael Zabarankin, Alexander Mafusalov, Stan Uryasev
For a random variable, superdistribution has emerged as a valuable probability concept. Similar to cumulative distribution function (CDF), it uniquely defines the random variable and can be evaluated with a simple one-dimensional minimization formula. This work leverages the structure of that formula to introduce buffered CDF (bCDF) and reduced CDF (rCDF) for random vectors. bCDF and rCDF are shown
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A mathematical programming approach for recognizing binet matrices Optim. Lett. (IF 1.6) Pub Date : 2023-10-06 Konstantinos Papalamprou, Leonidas Pitsoulis, Balász Kotnyek
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Linear-size formulations for connected planar graph partitioning and political districting Optim. Lett. (IF 1.6) Pub Date : 2023-10-05 Jack Zhang, Hamidreza Validi, Austin Buchanan, Illya V. Hicks
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On maximal and minimal elements for sets with respect to cones Optim. Lett. (IF 1.6) Pub Date : 2023-10-04 A. Farajzadeh
An existence theorem of maximum points for a set preordered (not necessarily partial ordered) by a convex cone of a real linear space is presented. The proof of the theorem is different from the usual technic, that is the separation theorem, as used in Khazayel and Farajzadeh (Optim Lett 15:847–858, 2021) and Araya (Appl Math Lett 22:501–504, 2009). The main result of this gives an affirmative answer
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Frugal and decentralised resolvent splittings defined by nonexpansive operators Optim. Lett. (IF 1.6) Pub Date : 2023-10-05 Matthew K. Tam
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Linear convergence rate analysis of proximal generalized ADMM for convex composite programming Optim. Lett. (IF 1.6) Pub Date : 2023-10-03 Han Wang, Yunhai Xiao
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A gradient-based bilevel optimization approach for tuning regularization hyperparameters Optim. Lett. (IF 1.6) Pub Date : 2023-09-29 Ankur Sinha, Tanmay Khandait, Raja Mohanty
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Prediction of annual CO2 emissions at the country and sector levels, based on a matrix completion optimization problem Optim. Lett. (IF 1.6) Pub Date : 2023-09-26 Francesco Biancalani, Giorgio Gnecco, Rodolfo Metulini, Massimo Riccaboni
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A neurodynamic approach for joint chance constrained rectangular geometric optimization Optim. Lett. (IF 1.6) Pub Date : 2023-09-26 Siham Tassouli, Abdel Lisser
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Recursive universum linear discriminant analysis Optim. Lett. (IF 1.6) Pub Date : 2023-09-29 Chun-Na Li, Jiakou Liu, Yanhui Meng, Yuan-Hai Shao
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Kernel $$\ell ^1$$ -norm principal component analysis for denoising Optim. Lett. (IF 1.6) Pub Date : 2023-09-25 Xiao Ling, Anh Bui, Paul Brooks
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Tensor denoising via dual Schatten norms Optim. Lett. (IF 1.6) Pub Date : 2023-09-27 Maryam Bagherian
Denoising is an important preprocessing step that can improve the quality of the data and make it more suitable for further analysis, enhance the performance of machine learning models, identify underlying patterns, reduce computation time, and make data more interpretable by humans. Here we propose a tensor denoising approach based on Pareto efficient pairs and its relation with dual norms. We relate
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A maximal-clique-based set-covering approach to overlapping community detection Optim. Lett. (IF 1.6) Pub Date : 2023-09-25 Michael J. Brusco, Douglas Steinley, Ashley L. Watts
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Deep reinforcement learning for approximate policy iteration: convergence analysis and a post-earthquake disaster response case study Optim. Lett. (IF 1.6) Pub Date : 2023-09-23 A. Gosavi, L. H. Sneed, L. A. Spearing
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Higher-order optimality conditions of robust Benson proper efficient solutions in uncertain vector optimization problems Optim. Lett. (IF 1.6) Pub Date : 2023-09-23 Qilin Wang, Jing Jin, Yuwen Zhai
In this article, we study higher-order optimality conditions of robust Benson proper efficient solutions in uncertain vector optimization problems. One first introduces a new epiderivative of set-valued maps, the higher-order weak radial epiderivative. Then we investigate some of its properties. The concept of robust Benson proper effective solutions is proposed for uncertain vector optimization problems
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On optimal universal first-order methods for minimizing heterogeneous sums Optim. Lett. (IF 1.6) Pub Date : 2023-09-22 Benjamin Grimmer
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Dependence in constrained Bayesian optimization Optim. Lett. (IF 1.6) Pub Date : 2023-09-20 Shiqiang Zhang, Robert M. Lee, Behrang Shafei, David Walz, Ruth Misener
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Special issue dedicated to the 8th International Conference on Variable Neighborhood Search (ICVNS 2021) Optim. Lett. (IF 1.6) Pub Date : 2023-09-21 Nenad Mladenović, Angelo Sifaleras, Andrei Sleptchenko
This special issue contains 15 papers submitted by the participants of the 8th International Conference on Variable Neighborhood Search (ICVNS 2021), which was held in Abu Dhabi, U.A.E., online due to COVID-19 restrictions, on March 22–24, 2021.
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A new feasible moving ball projection algorithm for pseudomonotone variational inequalities Optim. Lett. (IF 1.6) Pub Date : 2023-09-19 Limei Feng, Yongle Zhang, Yiran He
The projection is often used in solving variational inequalities. When projection onto the feasible set is not easy to calculate, the projection algorithms are replaced by the relaxed projection algorithms. However, these relaxed projection algorithms are not feasible, and to ensure the convergence of these relaxed projection algorithms, in addition to assuming some basic conditions, such as the Slater
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Approximation algorithm for solving the 1-line Steiner tree problem with minimum number of Steiner points Optim. Lett. (IF 1.6) Pub Date : 2023-09-18 Suding Liu
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Convergence analysis of block majorize-minimize subspace approach Optim. Lett. (IF 1.6) Pub Date : 2023-09-16 Emilie Chouzenoux, Jean-Baptiste Fest
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Optimal step length for the maximal decrease of a self-concordant function by the Newton method Optim. Lett. (IF 1.6) Pub Date : 2023-08-16 Anastasia Ivanova, Roland Hildebrand
In this paper we consider the problem of finding the optimal step length for the Newton method on the class of self-concordant functions, with the decrease in function value as criterion. We formulate this problem as an optimal control problem and use optimal control theory to solve it.