ABSTRACT For those acquainted with CVX (aka disciplined convex programming) of Grant and Boyd (Matlab software for disciplined convex programming, version 2.2, CVX Research, Inc., 2020. http://cvxr.com/cvx/doc/), the motivation of this work is the desire to extend the scope of CVX beyond convex minimization – to convex–concave saddle point problems and variational inequalities with monotone operators

In this paper, we propose a general algorithmic framework for the first-order methods in optimization in a broad sense, including minimization problems, saddle-point problems and variational inequalities (VIs). This framework allows obtaining many known methods as a special case, the list including accelerated gradient method, composite optimization methods, level-set methods, Bregman proximal methods

In this paper, we consider reflected three-operator splitting methods for monotone inclusion problems in real Hilbert spaces. To do this, we first obtain weak convergence analysis and nonasymptotic O(1/n) convergence rate of the reflected Krasnosel'skiĭ-Mann iteration for finding a fixed point of nonexpansive mapping in real Hilbert spaces under some seemingly easy to implement conditions on the iterative

(2021). Preface. Optimization Methods and Software: Vol. 36, Special issue dedicated to the memory of Professor Masao Iri. Guest Editors: Takashi Tsuchiya and Kokichi Sugihara, pp. 259-261.

(2021). Correction. Optimization Methods and Software: Vol. 36, Special issue dedicated to the memory of Professor Masao Iri. Guest Editors: Takashi Tsuchiya and Kokichi Sugihara, pp. 669-669.

In this paper, we introduce an efficient subgradient extragradient (SE) based method for solving variational inequality problems with monotone operator in Hilbert space. In many existing SE methods, two values of operator are needed over each iteration and the Lipschitz constant of the operator or linesearch is required for estimating step sizes, which are usually not practical and expensive. To overcome

We propose an inexact nonmonotone successive quadratic programming (SQP) algorithm for solving nonlinear programming problems with equality constraints and bounded variables. Regarding the value of the current feasibility violation and the minimum value of its linear approximation over a trust region, several scenarios are envisaged. In one scenario, a possible infeasible stationary point is detected

This paper proposes a globally convergent regularized interior point method that involves a specifically designed regularization strategy for constrained optimization. The main concept of the proposed algorithm is that when a proper regularization parameter is selected, the direction obtained from the regularized barrier equation is a descent direction for either the objective function or constraint

In the past, combinatorial structures have been used only to tune parameters of neural networks. In this work, we employ neural networks in the form of Boltzmann machines and Hopfield networks for the construction of a specific class of combinatorial designs, namely covering arrays (CAs). In past works, these neural networks were successfully used to solve set cover instances. For the construction

We consider the weighted linear complementarity problem (denoted by wLCP). Many numerical algorithms have been proposed for solving the monotone wLCP. In this paper, we propose a damped Gauss–Newton method to solve the non-monotone wLCP which is designed based on a derivative-free non-monotone line search. We show that the proposed method is well defined and it is globally convergent without any problem

The Shortest Path Problem (SPP) is among the most studied problems in Operations Research, for its theoretical aspects but also because it appears as sub-problem in many combinatorial optimization problems, e.g. Vehicle Routing and Maximum Flow-Minimum Cost problems. Given a sequence of SPPs, suppose that two subsequent instances solely differ by a slight change in the graph structure: that is the

Min–max problems have broad applications in machine learning, including learning with non-decomposable loss and learning with robustness to data distribution. Convex–concave min–max problem is an active topic of research with efficient algorithms and sound theoretical foundations developed. However, it remains a challenge to design provably efficient algorithms for non-convex min–max problems with

ABSTRACT This paper attempts to study loan interest rate Nash game models in the banking sector under regulatory solvency constraints. By taking solvency constraints as Basel I, Basel II, and Expected Shortfall (ES), we obtain results regarding the existence of loan interest rate equilibrium. A sensitivity analysis for solvency models and some numerical results are presented. Numerical results show

In order to generate properly efficient solutions of a general multiobjective optimization problem, a unified direction approach was introduced by Ghaznavi et al. (Optim. Methods Softw., 2019). However, there are some deficiencies in this work, and a counterexample is given to show that properly efficient solutions of a general multiobjective optimization problem cannot be obtained via optimal solutions

ABSTRACT This paper presents a C++ application programming interface for a co-evolutionary algorithm for solution and scenario generation in stochastic problems. Based on a two-space biased random-key genetic algorithm, it involves two types of populations that are mutually impacted by the fitness calculations. In the solution population, high-quality solutions evolve, representing first-stage decisions

We analyse the scaling matrix, search direction, and neighbourhood used in MOSEK's algorithm for nonsymmetric conic optimization [J. Dahl and E.D. Andersen, A primal-dual interior-point algorithm for nonsymmetric exponential-cone optimization, preprint (2019)]. It is proven that these can be used to compute a near-optimal solution to the homogeneous self-dual model in polynomial time. This provides

ABSTRACT We present an algorithm for approximately solving bounded convex vector optimization problems. The algorithm provides both an outer and an inner polyhedral approximation of the upper image. It is a modification of the primal algorithm presented by Löhne, Rudloff, and Ulus in 2014. There, vertices of an already known outer approximation are successively cutoff to improve the approximation error

This paper is concerned with two variants of the inverse uncapacitated facility location problem with service cost modifications on tree networks under the rectilinear norm. In the first variant, the aim is to modify the underlying service costs in the cheapest possible way with respect to modification bounds such that a set of predetermined facility locations becomes optimal under the new parameters

This paper is concerned with two variants of the inverse uncapacitated facility location problem with service cost modifications on tree networks under the rectilinear norm. In the first variant, the aim is to modify the underlying service costs in the cheapest possible way with respect to modification bounds such that a set of predetermined facility locations becomes optimal under the new parameters

We suppose the existence of an oracle which solves any semidefinite programming (SDP) problem satisfying strong feasibility (i.e. Slater's condition) simultaneously at its primal and dual sides. We note that such an oracle might not be able to directly solve general SDPs even after certain regularization schemes are applied. In this work we fill this gap and show how to use such an oracle to ‘completely

ABSTRACT In this paper, we consider gradient methods for minimizing smooth convex functions, which employ the information obtained at the previous iterations in order to accelerate the convergence towards the optimal solution. This information is used in the form of a piece-wise linear model of the objective function, which provides us with much better prediction abilities as compared with the standard

In a recent paper, Avis, Bremner, Tiwary and Watanabe gave a method for constructing linear programs (LPs) based on algorithms written in a simple programming language called Sparks. If an algorithm produces the solution x to a problem in polynomial time and space then the LP constructed is also of polynomial size and its optimum solution contains x as well as a complete execution trace of the algorithm

Over the last two decades, the PageRank problem has received increased interest from the academic community as an efficient tool to estimate web-page importance in information retrieval. Despite numerous developments, the design of efficient optimization algorithms for the PageRank problem is still a challenge. This paper proposes three new algorithms with a linear time complexity for solving the problem

ABSTRACT We propose structured spectral gradient algorithms for solving nonlinear least squares problems based on a modified structured secant equation. The idea was to integrate more details of the Hessian of the objective function into the standardized spectral parameters with the goal of improving numerical efficiency. We safeguard the structured spectral parameters to avoid negative curvature search

ABSTRACT In this paper, we analyse the Basic Tensor Methods, which use approximate solutions of the auxiliary problems. The quality of this solution is described by the residual in the function value, which must be proportional to ϵ p + 1 p , where p ≥   1 is the order of the method and ϵ is the desired accuracy in the main optimization problem. We analyse in details the auxiliary schemes for the third-

ABSTRACT Piecewise linear L 1 -regression problem is formulated as an unconstrained difference of convex (DC) optimization problem and an algorithm for solving this problem is developed. Auxiliary problems are introduced to design an adaptive approach to generate a suitable piecewise linear regression model and starting points for solving the underlying DC optimization problems. The performance of

ABSTRACT In this paper, we first extend the diminishing stepsize method for nonconvex constrained problems presented in F. Facchinei, V. Kungurtsev, L. Lampariello and G. Scutari [Ghost penalties in nonconvex constrained optimization: Diminishing stepsizes and iteration complexity, To appear on Math. Oper. Res. 2020. Available at https://arxiv.org/abs/1709.03384.] to deal with equality constraints

ABSTRACT A stochastic second-order trust region method is proposed, which can be viewed as an extension of the trust-region-ish (TRish) algorithm proposed by Curtis et al. [A stochastic trust region algorithm based on careful step normalization. INFORMS J. Optim. 1(3) 200–220, 2019]. In each iteration, a search direction is computed by (approximately) solving a subproblem defined by stochastic gradient

ABSTRACT Updating the augmented Lagrangian multiplier by closed-form expression yields efficient first-order infeasible approach for optimization problems with orthogonality constraints. Hence, parallelization becomes tractable in solving this type of problems. Inspired by this closed-form updating scheme, we propose a novel penalty function with compact convex constraints (PenC). We show that PenC

ABSTRACT To solve a large-scale unconstrained optimization problem, in this paper we propose a class of spectral three-term conjugate gradient methods. We indicate that the proposed class, in fact, generates sufficient descent directions and also fulfill Dai–Liao conjugacy condition. We prove the global convergence of the presented class for either uniformly convex or general smooth functions under

ABSTRACT Two-stage stochastic models give rise to very large optimization problems. Several approaches have been devised for efficiently solving them, including interior-point methods (IPMs). However, using IPMs, the linking columns associated with first-stage decisions cause excessive fill-in for the solution of the normal equations. This downside is usually alleviated if variable splitting is applied

In an application of map labelling to air-traffic control, labels should be placed with as few overlaps as possible since labels include important information about airplanes. Motivated by this application, de Berg and Gerrits (Comput. Geom. 2012) proposed a problem of maximizing the number of free labels (i.e. labels not intersecting with any other label) and developed approximation algorithms for

Complementarity problems are often used to compute equilibria made up of specifically coordinated solutions of different optimization problems. Specific examples are game-theoretic settings like the bimatrix game or energy market models like for electricity or natural gas. While optimization under uncertainties is rather well-developed, the field of equilibrium models represented by complementarity

ABSTRACT We analyse the existence of Nash equilibria for a class of quadratic multi-leader-follower games using the nonsmooth best response function. To overcome the challenge of nonsmoothness, we pursue a smoothing approach resulting in a reformulation as a smooth Nash equilibrium problem. The existence and uniqueness of solutions are proven for all smoothing parameters. Accumulation points of Nash

Utility-based shortfall risk measure (SR) has received increasing attentions over the past few years. Recently Delage et al. [Shortfall Risk Models When Information of Loss Function Is Incomplete, GERAD HEC, Montréal, 2018] consider a situation where a decision maker's true loss function in the definition of SR is unknown but it is possible to elicit a set of plausible utility functions with partial

ABSTRACT Given the inability to foresee all possible scenarios, it is justified to desire an efficient trust-region subproblem solver capable of delivering any desired level of accuracy on demand; that is, the accuracy obtainable for a given trust-region subproblem should not be partially dependent on the problem itself. Current state-of-the-art iterative eigensolvers all fall into the class of restarted

Tom Streubel has observed that for functions in abs-normal form, generalized Taylor expansions of arbitrary order d¯−1 can be generated by algorithmic piecewise differentiation. Abs-normal form means that the real or vector valued function is defined by an evaluation procedure that involves the absolute value function |⋅| apart from arithmetic operations and d¯ times continuously differentiable univariate

Among many approaches to increase the computational efficiency of semidefinite programming (SDP) relaxation for nonconvex quadratic constrained quadratic programming problems (QCQPs), exploiting the aggregate sparsity of the data matrices in the SDP by Fukuda et al. [Exploiting sparsity in semidefinite programming via matrix completion I: General framework, SIAM J. Optim. 11(3) (2001), pp. 647–674]

In this paper, we consider the problem of finding ε-approximate stationary points of convex functions that are p-times differentiable with ν-Hölder continuous pth derivatives. We present tensor methods with and without acceleration. Specifically, we show that the non-accelerated schemes take at most O ϵ − 1 / ( p + ν − 1 ) iterations to reduce the norm of the gradient of the objective below given ϵ

ABSTRACT In this paper, we propose a new framework to implement interior point method (IPM) in order to solve some very large-scale linear programs (LPs). Traditional IPMs typically use Newton's method to approximately solve a subproblem that aims to minimize a log-barrier penalty function at each iteration. Due its connection to Newton's method, IPM is often classified as second-order method – a genre

In this paper, we consider Newton projection method for solving the quadratic programming problem that emerges in simulation of joining process for assembly with compliant parts. This particular class of problems has specific features such as an ill-conditioned Hessian and a sparse matrix of constraints as well as a requirement for the large-scale computations. We use the projected Newton method with

This paper deals with minimax fractional programs whose objective functions are the maximum of finite ratios of convex functions, with arbitrary convex constraints set. For such problems, Dinkelbach-type algorithms fail to work since the parametric subproblems may be nonconvex, whereas the latter need a global optimal solution of these subproblems. We give necessary optimality conditions for such problems

The precedence constrained traveling salesman (TSP-PC), also known as sequential ordering problem (SOP), consists of finding an optimal tour that satisfies the namesake constraints. Mixed integer-linear programming works well with the ‘lightly constrained’ TSP-PCs, close to asymmetric TSP, as well as the with the ‘heavily constrained’ (Gouveia, Ruthmair, 2015). Dynamic programming (DP) works well with

Discrete convex functions are used in many areas, including operations research, discrete-event systems, game theory, and economics. The objective of this paper is to investigate basic operations such as direct sum, splitting, and aggregation that are related to network induction of discrete convex functions as well as discrete convex sets. Various kinds of discrete convex functions in discrete convex

We develop and analyse a variant of the SARAH algorithm, which does not require computation of the exact gradient. Thus this new method can be applied to general expectation minimization problems rather than only finite sum problems. While the original SARAH algorithm, as well as its predecessor, SVRG, requires an exact gradient computation on each outer iteration, the inexact variant of SARAH (iSARAH)

This paper examines a class of cooperative differential games with continuous updating. Here it is assumed that at each time instant players have or use information about the game structure defined for a closed time interval with fixed duration. The current time continuously evolves with the updating interval. The main problems considered in a cooperative setting with continuous updating is how to

In this paper, we consider a joint-inversion problem using different types of geophysical data: gravity and magnetism. We first formulate two kinds of inverse problems in the famework of the first kind Fredholm integral equations, and then build up a sparse inversion model combining the two inverse problems as well as the cross-gradient term. The cyclic gradient method for quadratic function minimization

This paper studies a class of two-stage distributionally robust optimization (TDRO) problems which comes from many practical application fields. In order to set up some implementable solution method, we first transfer the TDRO problem to its equivalent robust counterpart (RC) by the duality theorem of optimization. The RC reformulation of TDRO is a semi-infinite stochastic programming. Then we construct

We introduce COCO, an open-source platform for Comparing Continuous Optimizers in a black-box setting. COCO aims at automatizing the tedious and repetitive task of benchmarking numerical optimization algorithms to the greatest possible extent. The platform and the underlying methodology allow to benchmark in the same framework deterministic and stochastic solvers for both single and multiobjective

This work presents a partitioned solution procedure to compute shape gradients in fluid–structure interaction (FSI) using black-box adjoint solvers. Special attention is paid to project the gradients onto the undeformed configuration due to the mixed Lagrangian–Eulerian formulation of large-deformation FSI in this work. The adjoint FSI problem is partitioned as an assembly of well-known adjoint fluid

In this paper, we propose a primal-dual interior point trust-region method for solving nonlinear semidefinite programming problems. The method consists of the outer iteration (SDPIP) that finds a Karush–Kuhn–Tucker (KKT) point and the inner iteration (SDPTR) that calculates an approximate barrier KKT point. Algorithm SDPTR combines a commutative class of Newton-like directions with the steepest descent

In this paper, we propose a primal-dual interior point trust-region method for solving nonlinear semidefinite programming problems. The method consists of the outer iteration (SDPIP) that finds a Karush–Kuhn–Tucker (KKT) point and the inner iteration (SDPTR) that calculates an approximate barrier KKT point. Algorithm SDPTR combines a commutative class of Newton-like directions with the steepest descent

An extensible open-source deterministic global optimizer (EAGO) programmed entirely in the Julia language is presented. EAGO was developed to serve the need for supporting higher-complexity user-defined functions (e.g. functions defined implicitly via algorithms) within optimization models. EAGO embeds a first-of-its-kind implementation of McCormick arithmetic in an Evaluator structure allowing for

We introduce the class of multistage stochastic optimization problems with a random number of stages. For such problems, we show how to write dynamic programming equations and how to solve these equations using the Stochastic Dual Dynamic Programming algorithm. Finally, we consider a portfolio selection problem over an optimization period of random duration. For several instances of this problem, we

We consider the distributionally robust optimization and show that computing the distributional worst-case is equivalent to computing the projection onto the canonical simplex with additional linear inequality. We consider several distance functions to measure the distance of distributions. We write the projections as optimization problems and show that they are equivalent to finding a zero of real-valued

Mathematical programs with vanishing constraints (MPVCs) are a class of nonlinear optimization problems with applications to various engineering problems such as truss topology design and robot motion planning. MPVCs are difficult problems from both a theoretical and numerical perspective: the combinatorial nature of the vanishing constraints often prevents standard constraint qualifications and optimality

A method for evaluating lexicographical directional (LD)-derivatives of functional programs is presented, extending previous methods to programs containing conditional branches and loops. A language for imperative programs is given, and conditions under which LD-derivatives can be calculated automatically for conditional branches and loops are described, along with a full description of the source

The complexity of solving feasibility problems is considered in this work. It is assumed that the constraints that define the problem can be divided into expensive and cheap constraints. At each iteration, the introduced method minimizes a regularized pth-order model of the sum of squares of the expensive constraints subject to the cheap constraints. Under a Hölder continuity property with constant

Extended formulations of (k,l)-sparsity matroids defined on graphs with n vertices and m edges are investigated by Iwata et al. [Extended formulations for sparsity matroids, Math. Program. 158 (2016), pp. 565–574]. This note interprets results on (k,l)-lower-truncated transversal polymatroids by the first author in 1983, from the viewpoint of extended formulations, and shows the same O(mn) bound when

For the Lagrangian-DNN relaxation of quadratic optimization problems (QOPs), we propose a Newton-bracketing method to improve the performance of the bisection-projection method implemented in BBCPOP [ACM Tran. Softw., 45(3):34 (2019)]. The relaxation problem is converted into the problem of finding the largest zero y∗ of a continuously differentiable (except at y∗) convex function g:R→R such that g(y)=0