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    30 December 2017, Volume 5 Issue 4
    Continuous Optimization
    Gradient and Hessian of Joint Probability Function with Applications on Chance-Constrained Programs
    L. Jeff Hong, Guang-Xin Jiang
    2017, 5(4):  431-455.  doi:10.1007/s40305-017-0154-6
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    Joint probability function refers to the probability function that requires multiple conditions to satisfy simultaneously. It appears naturally in chanceconstrained programs. In this paper, we derive closed-form expressions of the gradient and Hessian of joint probability functions and develop Monte Carlo estimators of them. We then design a Monte Carlo algorithm, based on these estimators, to solve chance-constrained programs. Our numerical study shows that the algorithm works well, especially only with the gradient estimators.

    On the Convergence Rate of an Inexact Proximal Point Algorithm for Quasiconvex Minimization on Hadamard Manifolds
    Nancy Baygorrea, Erik Alex Papa Quiroz, Nelson Maculan
    2017, 5(4):  457-467.  doi:10.1007/s40305-016-0129-z
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    In this paper, we present an analysis about the rate of convergence of an inexact proximal point algorithm to solve minimization problems for quasiconvex objective functions on Hadamard manifolds.We prove that under natural assumptions the sequence generated by the algorithm converges linearly or superlinearly to a critical point of the problem.

    Approximate Optimality Conditions for Composite Convex Optimization Problems
    Xian-Jun Long, Xiang-Kai Sun, Zai-Yun Peng
    2017, 5(4):  469-485.  doi:10.1007/s40305-016-0140-4
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    The purpose of this paper is to study the approximate optimality condition for composite convex optimization problems with a cone-convex system in locally convex spaces, where all functions involved are not necessarily lower semicontinuous.By using the properties of the epigraph of conjugate functions, we introduce a new regularity condition and give its equivalent characterizations. Under this new regularity condition, we derive necessary and sufficient optimality conditions of ε-optimal solutions for the composite convex optimization problem. As applications of our results, we derive approximate optimality conditions to cone-convex optimization problems. Our results extend or cover many known results in the literature.

    A New Restarting Adaptive Trust-Region Method for Unconstrained Optimization
    Morteza Kimiaei, Susan Ghaderi
    2017, 5(4):  487-507.  doi:10.1007/s40305-016-0149-8
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    In this paper, we present a new adaptive trust-region method for solving nonlinear unconstrained optimization problems. More precisely, a trust-region radius based on a nonmonotone technique uses an approximation of Hessian which is adaptively chosen. We produce a suitable trust-region radius; preserve the global convergence under classical assumptions to the first-order critical points; improve the practical performance of the new algorithm compared to other exiting variants.Moreover, the quadratic convergence rate is established under suitable conditions. Computational results on the CUTEst test collection of unconstrained problems are presented to show the effectiveness of the proposed algorithm compared with some exiting methods.

    The Study of Group Scheduling Problems with General Dual-Position-Based Job Processing Times
    Xian-Yu Yu· De-Qun Zhou · Yu-Lin Zhang
    2017, 5(4):  509-527.  doi:10.1007/s40305-017-0159-1
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    Scheduling with group technology has been a vivid research area in the past decades. However, group technology with general dual-effect variable processing times needs to be further explored although this kind of group technology plays an important role in some actual manufacturing scenarios. Accordingly, this paper considers group scheduling problems with a kind of general group variable processing times model, where the actual processing time of each job in group is variable due to the dual effect of both the job position and the group position. The objectives of two types of considered problems are to minimize the makespan and the total completion time, respectively. Based on the decomposition analysis, the mathematical logic analysis and the computational complexity proof, it is obtained that the makespan minimization problem and the total completion time minimization problem are both polynomially solvable under the condition that the group number is constant. For
    three special cases of considered problems, polynomial solving algorithms with lower computational complexity are proposed.

    Continuous Optimization
    A Partially Parallel Prediction-Correction Splitting Method for Convex Optimization Problems with Separable Structure
    Fu-Sheng Bai· Ling Xu
    2017, 5(4):  529-544.  doi:10.1007/s40305-017-0163-5
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    In this paper, we propose a partially parallel prediction-correction splitting method for solving block-separable linearly constrained convex optimization problems with three blocks. Unlike the extended alternating direction method of multipliers, the last two subproblems in the prediction step are solved parallelly, and a correction step is employed in the method to correct the dual variable and two blocks of the primal variables. The step size adapted in the correction step allows for major contribution from the latest solution point to the iteration point. Some numerical results are reported to show the effectiveness of the presented method.

    An Inexact Proximal Method with Proximal Distances for Quasimonotone Equilibrium Problems
    Lennin Mallma Ramirez · Erik Alex Papa Quiroz ·P. R. Oliveira
    2017, 5(4):  545-561.  doi:10.1007/s40305-017-0156-4
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    In this paper, we propose an inexact proximal point method to solve equilibrium problems using proximal distances and the diagonal subdifferential. Under some natural assumptions on the problem and the quasimonotonicity condition on the bifunction, we prove that the sequence generated by the method converges to a solution point of the problem.