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    30 September 2018, Volume 6 Issue 3
    Continuous Optimization
    Computation of Fisher–Gale Equilibrium by Auction
    Yurii Nesterov, Vladimir Shikhman
    2018, 6(3):  349-390.  doi:https://doi.org/10.1007/s40305-018-0195-5
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    We study the Fisher model of a competitive market from the algorithmic perspective. For that, the related convex optimization problem due to Gale and Eisenberg (Ann Math Stat 30(1):165–168,1959) is used. The latter problem is known to yield a Fisher equilibrium under some structural assumptions on consumers’ utilities, e.g., homogeneity of degree 1, homotheticity. Our goal is to examine applicability of the convex optimization framework by departing from these traditional assumptions. We just assume the concavity of consumers’ utility functions. For this case, we suggest a novel concept of Fisher–Gale equilibrium by using consumers’ utility prices. The prices of utility transfer the utility of consumption bundle to a common numéraire. We develop a subgradient-type algorithm from Convex Analysis to compute a Fisher–Gale equilibrium via Gale’s approach. In order to decentralize prices, we additionally implement the auction design, i.e., consumers settle and update their individual prices and producers sell at the highest offer price. Our price adjustment is based on a tatonnement procedure, i.e., the prices change proportionally to consumers’ individual excess supplies. Historical averages of consumption are shown to clear the market of goods. Our algorithm is justified by a global rate of convergence. In the worst case, the number of price updates needed to achieve an -tolerance is proportional to .

    Global Complexity Bound of the Inexact Levenberg–Marquardt Method
    Jian-Chao Huang, Jin-Yan Fan
    2018, 6(3):  417-428.  doi:https://doi.org/10.1007/s40305-017-0184-0
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    In this paper, we investigate the global complexity bound for the inexact Levenberg–Marquardt method, where the Jacobian may be perturbed and the solution is possibly not exact. Under reasonable assumptions, we show that the global complexity bound is , which is the same as the exact case. We also show that it can be reduced to under some regularity assumption.

    A Stochastic Level-Value Estimation Method for Global Optimization
    Hong-Bin Yu, Wei-Jia Zeng, Dong-Hua Wu
    2018, 6(3):  429-444.  doi:https://doi.org/10.1007/s40305-017-0175-1
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    In this paper, we propose a stochastic level-value estimation method to solve a kind of box-constrained global optimization problem. For this purpose, we first derive a generalized variance function associated with the considered problem and prove that the largest root of the function is the global minimal value. Then, Newton’s method is applied to find the root. The convergence of the proposed method is established under some suitable conditions. Based on the main idea of the cross-entropy method to update the sampling density function, an important sampling technique is proposed in the implementation. Preliminary numerical experiments indicate the validity of the proposed method.

    On Globally Q-Linear Convergence of a Splitting Method for Group Lasso
    Yun-Da Dong, Hai-Bin Zhang, Huan Gao
    2018, 6(3):  445-454.  doi:https://doi.org/10.1007/s40305-017-0176-0
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    In this paper, we discuss a splitting method for group Lasso. By assuming that the sequence of the step lengths has positive lower bound and positive upper bound (unrelated to the given problem data), we prove its Q-linear rate of convergence of the distance sequence of the iterates to the solution set. Moreover, we make comparisons with convergence of the proximal gradient method analyzed very recently.

    Discrete Optimization
    Online Scheduling on Bounded Batch Machines to Minimize the MaximumWeighted Completion Time
    Wen-Hua Li, Xing Chai
    2018, 6(3):  455-466.  doi:https://doi.org/10.1007/s40305-017-0179-x
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    We investigate the online scheduling problem on identical parallel-batch machines to minimize the maximum weighted completion time. In this problem, jobs arrive over time and the processing times (of the jobs) are identical, and the batch capacity is bounded. For this problem, we provide a best possible online algorithm with a competitive ratio of . Moreover, when restricted to dense-algorithms, we present a best possible dense-algorithm with a competitive ratio of 2.

    Continuous Optimization
    Spectral Properties and Optimality for Elementary Matrices
    Ricardo Biloti, Jo?o Daniel Palma Ramos,Jin-Yun Yuan
    2018, 6(3):  467-472.  doi:https://doi.org/10.1007/s40305-017-0177-z
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    A generalization of the Householder transformation, renamed as elementary matrix by A.S. Householder: Unitary transformation of a nonsymmetric matrix, J. ACM, 5(4), 339–342, 1958, was introduced by LaBudde (Math Comput 17(84):433–437, 1963) as a tool to obtain a tridiagonal matrix similar to a given square matrix. Some of the free parameters of the transformation can be chosen to attain better numerical properties. In this work, we study the spectral properties of the transformation. We also propose a special choice for free coefficients of that transformation to minimize its condition number. The transformation with such suitable choice of parameters is called optimal.

    Discrete Optimization
    Competitive Project Scheduling on Two Unbounded Parallel Batch Machines
    Ling-Fa Lu, Li-Qi Zhang
    2018, 6(3):  473-483.  doi:https://doi.org/10.1007/s40305-017-0180-4
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    This paper considers competitive project scheduling on two unbounded parallel batch machines. There are two competing firms, and each firm has an unbounded parallel batch machine. All projects must be performed in batches by Firms 1 and 2 on their machines, respectively. The profit that each firm obtains from each project depends on whether the firm finishes the job before or after its competitor. In the first problem, given a feasible schedule for Firm 1, the objective is to find an optimal schedule to maximize the total reward for Firm 2 under the given schedule for Firm 1. The corresponding total reward for Firm 1 is called the worst-case total reward of the given schedule for Firm 1. In the second problem, the objective is to find an optimal schedule for Firm 1 to maximize the worst-case total reward. We provide optimal algorithms for the two problems, respectively.