In this paper, we study optimal control problems containing ordinary control systems, linear with respect to a control variable, described by fractional Dirichlet and Dirichlet–Neumann Laplace operators and a nonlinear integral performance index. The main result is a theorem on the existence of optimal solutions for such problems. In our approach we use a characterization of a weak lower semicontinuity

In this study, we introduce the Traveling Car Renter with Passengers (CaRSP), an extension of the Traveling Car Renter Problem (CaRS). The latter generalizes the Traveling Salesman Problem (TSP) by allowing several cars with different costs to be available for use during the salesman’s tour. In the CaRSP, passengers are allowed in the salesman’s car and share trip expenses with the driver. Passengers

This paper investigates the combinatorial nonlinear programming model that DiDi proposed for solving their driver-order matching problem. The model is reformulated to an equivalent continuous nonlinear program which is amenable to efficient commercial solvers. A backward induction procedure for computing the lower bound is also proposed. Computational experiments demonstrate that the local solution

A classic result of Banach states that the supreme of a multivariate homogenous polynomial is equivalent to that of its associated symmetric multilinear form over unit balls. Using the language of higher-order tensors in finite-dimensional spaces, this means that for a symmetric tensor, its largest singular value is in fact equivalent to the largest magnitude of its eigenvalues. This note strengthens

We extend and improve recent results given by Singh and Watson on using classical bounds on the union of sets in a chance-constrained optimization problem. Specifically, we revisit the so-called Dawson and Sankoff bound that provided one of the best approximations of a chance constraint in the previous analysis. Next, we show that our work is a generalization of the previous work, and in fact the inequality

In this paper we introduce a new parameterized Quadratic Decision Rule (QDR), a generalisation of the commonly employed Affine Decision Rule (ADR), for two-stage linear adjustable robust optimization problems with ellipsoidal uncertainty and show that (affinely parameterized) linear adjustable robust optimization problems with QDRs are numerically tractable by presenting exact semi-definite program

In this paper, we show that the weighted vertex coloring problem can be solved in polynomial on the sum of vertex weights time for \(\{P_5,K_{2,3}, K^+_{2,3}\}\)-free graphs. As a corollary, this fact implies polynomial-time solvability of the unweighted vertex coloring problem for \(\{P_5,K_{2,3},K^+_{2,3}\}\)-free graphs. As usual, \(P_5\) and \(K_{2,3}\) stands, respectively, for the simple path

A popular method for finding the projection onto the intersection of two closed convex subsets in Hilbert space is Dykstra’s algorithm. In this paper, we provide sufficient conditions for Dykstra’s algorithm to converge rapidly, in finitely many steps. We also analyze the behaviour of Dykstra’s algorithm applied to a line and a square. This case study reveals stark similarities to the method of alternating

In this paper, we present an improved methodology to compute the \(\omega \)-primality of a numerical semigroup. The approach is based on exploiting the structure of the problem on a resolution method for optimizing a linear function over the set of efficient solutions of a multiple objective integer linear programming problem. The numerical experiments show the efficiency of the proposed technique

We decompose the copositive cone \(\mathcal {COP}^{n}\) into a disjoint union of a finite number of open subsets \(S_{{\mathcal {E}}}\) of algebraic sets \(Z_{{\mathcal {E}}}\). Each set \(S_{{\mathcal {E}}}\) consists of interiors of faces of \(\mathcal {COP}^{n}\). On each irreducible component of \(Z_{{\mathcal {E}}}\) these faces generically have the same dimension. Each algebraic set \(Z_{{\mathcal

We study a variant of the competitive facility location problem, in which a company is to locate new facilities in a market where competitor’s facilities already exist. We consider the scenario where only a limited number of possible attractiveness levels is available, and the company has to select exactly one level for each open facility. The goal is to decide the facilities’ locations and attractiveness

In this work, we propose a new modified Popov’s method by using inertial effect for solving the variational inequality problem in real Hilbert spaces. The advantage of the proposed algorithm is the computation of only one value of the inequality mapping and one projection onto the admissible set per one iteration as well as it does not need to the prior knowledge of the Lipschitz constants of the variational

The Curriculum Based university Course Timetabling (CCT) problem consists in determining the best scheduling of university course lessons in a given time interval, assigning the lessons of each course to classrooms and time periods, so that a series of constraints is satisfied. These constraints are divided into two categories: hard constraints, necessary so that the programming can actually be implemented

In this paper, we consider the k-sink location problem in a path network with the goal of optimizing a combinational function of the maximum completion time and the total completion time. Let \(P=\left( V,E\right) \) be an undirected path network with n vertices. Each vertex has a positive weight, indicating the initial amount of supplies, and each edge has a positive length and a uniform capacity

This short paper describes a simple subgradient-based techniques for deriving bounds on the optimal solution value when using the ADMM to solve convex optimization problems. The technique requires a bound on the magnitude of some optimal solution vector, but is otherwise completely general. Some computational examples using LASSO problems demonstrate that the technique can produce steadily converging

Originally developed in 1954, positive bases and positive spanning sets have been found to be a valuable concept in derivative-free optimization (DFO). The quality of a positive basis (or positive spanning set) can be quantified via the cosine measure and convergence properties of certain DFO algorithms are intimately linked to the value of this measure. However, it is unclear how to compute the cosine

The aggregate subgradient method is developed for solving unconstrained nonsmooth difference of convex (DC) optimization problems. The proposed method shares some similarities with both the subgradient and the bundle methods. Aggregate subgradients are defined as a convex combination of subgradients computed at null steps between two serious steps. At each iteration search directions are found using

This note advances knowledge of the threshold of prox-boundedness of a function; an important concern in the use of proximal point optimization algorithms and in determining the existence of the Moreau envelope of the function. In finite dimensions, we study general prox-bounded functions and then focus on some useful classes such as piecewise functions and Lipschitz continuous functions. The thresholds

This paper mainly investigates the locally Lipschitz optimization problem (LLOP) with \(l_0\)-regularization in a finite dimensional space, which is generally NP-hard but highly applicable in statistics, compressed sensing and deep learning. First, we introduce two classes of stationary points for this problem: subdifferential-stationary point and proximal-stationary point. Secondly, based on these

In this paper, we propose a regularization projection method for solving a bilevel variational inequality problem in a Hilbert space. We first describe how to incorporate the regularization technique and the modified subgradient extragradient—like method, and then establish the strong convergence of the resulting algorithm under some suitable conditions. The new algorithm requires to compute only one

In this paper, we propose and study a new notion of local error bounds for a convex inequalities system defined in terms of a minimal time function. This notion is called generalized local error bounds with respect to F, where F is a closed convex subset of the Euclidean space \({\mathbb {R}}^n\) satisfying \(0\in F\). It is worth emphasizing that if F is a spherical sector with the apex at the origin

This paper presents a new mathematical programming model and a solution approach for a special class of graph partitioning problem. The problem studied here is in the context of distributed web search, in which a very large world-wide-web graph is partitioned to improve the efficiency of webpage ranking (known as PageRank). Although graph partitioning problems have been widely studied and there have

For single-commodity networks, the increase of the price of anarchy is bounded by a factor of \((1+\varepsilon )^p\) from above, when the travel demand is increased by a factor of \(1+\varepsilon \) and the latency functions are polynomials of degree at most p. We show that the same upper bound holds for multi-commodity networks and provide a lower bound as well.

This works investigates a conceptual algorithm for computing critical points of nonsmooth nonconvex optimization problems whose objective function is the sum of two locally Lipschitzian (component) functions. We show that upon additional assumptions on the component functions and or feasible set, the given algorithm extends several well-known optimization methods.

We study decision dependent distributionally robust optimization models, where the ambiguity sets of probability distributions can depend on the decision variables. These models arise in situations with endogenous uncertainty. The developed framework includes two-stage decision dependent distributionally robust stochastic programming as a special case. Decision dependent generalizations of five types

This paper aims at presenting the multiple depot vehicle scheduling problem with heterogeneous fleet and time windows (MDHFVSP-TW). We used a time-space network (TSN) to perform the modeling of MDHFVSP-TW, along with two methodologies to reduce its size and, therefore, its complexity. Along with size reduction methods, a mixed integer programming (MIP) heuristic with variable fixation was presented

Surrogate model assisted evolutionary algorithms (SAEAs) are metamodel-based strategies usually employed on the optimization of problems that demand a high computational cost to be evaluated. SAEAs employ metamodels, like Kriging and radial basis function (RBF), to speed up convergence towards good quality solutions and to reduce the number of function evaluations. However, investigations concerning

In this paper, we propose a theoretical framework of a predictor-corrector interior-point method for linear optimization based on the one-norm wide neighborhood of the central path, focusing on infeasible corrector steps. Here, we call the predictor-infeasible corrector algorithm. At each iteration, the proposed algorithm computes an infeasible corrector step in addition to the Ai-Zhang search directions

We consider Pareto-scheduling with two competing agents A and B on a single machine, in which each job has a positional due index and a deadline. The jobs of agents A and B are called the A-jobs and B-jobs, respectively, where the A-jobs have a common processing time, while the B-jobs are restricted by their precedence constraint. The objective is to minimize a general sum-form objective function of

We study a scheduling problem with linear deterioration of job processing times. We consider parallel identical machines. The objective is minimum total load, i.e., minimum total processing times on all the machines. We propose a simple heuristic of a greedy type for this NP-hard problem. A lower bound based on the geometric mean of the job deterioration factors is also introduced. The greedy heuristic

The paper is concerned with multiobjective sparse optimization problems, i.e. the problem of simultaneously optimizing several objective functions and where one of these functions is the number of the non-zero components (or the \(\ell _0\)-norm) of the solution. We propose to deal with the \(\ell _0\)-norm by means of concave approximations depending on a smoothing parameter. We state some equivalence

Supply chain simulation models are widely used for assessing supply chain performance and analyzing supply chain decisions. In combination with derivative-free optimization algorithms, simulation models have shown great potential in effective decision-making. Most of the derivative-free optimization algorithms, however, assume continuity of the response, which may not be true in some practical applications

Adjustable robust optimization (ARO) involves recourse decisions (i.e. reactive actions after the realization of the uncertainty, ‘wait-and-see’) as functions of the uncertainty, typically posed in a two-stage stochastic setting. Solving the general ARO problems is challenging, therefore ways to reduce the computational effort have been proposed, with the most popular being the affine decision rules

Optimization of simulation-based or data-driven systems is a challenging task, which has attracted significant attention in the recent literature. A very efficient approach for optimizing systems without analytical expressions is through fitting surrogate models. Due to their increased flexibility, nonlinear interpolating functions, such as radial basis functions and Kriging, have been predominantly

Single-commodity network design considers an edge-weighted, undirected graph with a supply/demand value at each node. It asks for minimum weight capacities such that each node can exactly send (or receive) its supply (or demand). In the robust variant, the supply or demand values may assume any realization in a given uncertainty set. One popular set is the well-known Hose polytope, which specifies

Obtaining guaranteed lower bounds for problems with unknown algebraic form has been a major challenge in derivative-free optimization. In this work, we present a deterministic global optimization method for black-box problems where the derivatives are not available or it is computationally expensive to obtain. However, a global upper bound on the diagonal Hessian elements is known. An edge-concave

We propose a novel robust optimization approach to analyze and optimize the expected performance of supply chain networks. We model uncertainty in the demand at the sink nodes via polyhedral sets which are inspired from the limit laws of probability. We characterize the uncertainty sets by variability parameters which control the degree of conservatism of the model, and thus the level of probabilistic

We present SUSPECT, an open source toolkit that symbolically analyzes mixed-integer nonlinear optimization problems formulated using the Python algebraic modeling library Pyomo. We present the data structures and algorithms used to implement SUSPECT. SUSPECT works on a directed acyclic graph representation of the optimization problem to perform: bounds tightening, bound propagation, monotonicity detection

The Particle Swarm Optimization (PSO) algorithm is a flexible heuristic optimizer that can be used for solving cardinality constrained binary optimization problems. In such problems, only K elements of the N-dimensional solution vector can be non-zero. The typical solution is to use a mapping function to enforce the cardinality constraint on the trial PSO solution. In this paper, we show that when

In a recent paper (Birgin et al. in Math Program 163(1):359–368, 2017), it was shown that, for the smooth unconstrained optimization problem, worst-case evaluation complexity \(O(\epsilon ^{-(p+1)/p})\) may be obtained by means of algorithms that employ sequential approximate minimizations of p-th order Taylor models plus \((p+1)\)-th order regularization terms. The aforementioned result, which assumes

In this note we present a barrier method for vector optimization problems with inequality constraints. To this aim, we firstly investigate some constraint qualification conditions and we compare them to the corresponding ones in literature. Then, we define a barrier function and observe that its basic properties do work for fairly general situations, while for meaningful convergence results of the

Due to time-varying utility prices, peak demand charges, and variable-efficiency equipment, optimal operation of heating ventilation, and air conditioning systems in campuses or large buildings is nontrivial. Given forecasts of ambient conditions and utility prices, system energy requirements can be reduced by optimizing heating/cooling load within buildings and then choosing the best combination of

This paper studies the bounded parallel-batching scheduling problem considering job rejection, deteriorating jobs, setup time, and non-identical job sizes. Each job will be either rejected with a certain penalty cost, or accepted and further processed in batches on a single machine. There is a setup time before processing each batch, and the objective is to minimize the sum of the makespan and the

The Multi-Depot Cumulative Capacitated Vehicle Routing Problem is a variation of the recently proposed Capacitated Cumulative Vehicle Routing Problem, where several depots can be considered as starting points of routes. Its objective aims at minimizing the sum of arrival times at customers for providing service. Practical considerations imply to address the delivery of customers from multiple depots

In this paper, we consider the inclusion problems for maximal monotone set-valued vector fields defined on Hadamard manifolds. We discuss the equivalence between nonemptiness of solution set of the inclusion problem and the coercivity condition. The boundedness of solution set of the inclusion problem is studied. An application of our results to optimization problems in Hadamard manifolds is also presented

This article presents a rectangular branch-and-bound algorithm with standard bisection rule for solving linear multiplicative problem (LMP). In this algorithm, a novel linear relaxation technique is presented for deriving the linear relaxation programming of problem LMP, which has separable characteristics and can be used to acquire the upper bound of the optimal value to problem LMP. Thus, to obtain

This paper is mainly devoted to the existence of solutions of generalized mixed variational inequalities (GMVI for short) with perturbation in reflexive Banach spaces. When the constraint set is weakly compact, we deduce two existence theorems for GMVI. Based on these two existence theorems, when the constraint set is unbounded, we obtain some existence theorems for GMVI perturbed by a nonlinear continuous

This paper presents a new linear reformulation to convert a binary quadratically constrained quadratic program into a 0–1 mixed integer linear program. By exploiting the symmetric structure of the quadratic terms embedded in the objective and constraint functions, the proposed linear reformulation requires fewer variables and constraints than other known O(n)-sized linear reformulations. Theoretical

Debottlenecking is highly desirable to increase the production throughput for the oil sands industry. In this work, the bottleneck identification and capacity expansion problem is solved through optimization techniques. In the proposed debottlenecking procedure, first-principles method and Gaussian process modeling approach are applied to build process models. Depending on the type of process model

This article proposes extensions of exact and heuristic dynamic programming algorithms for the traveling salesman problem with flexible time windows, which are a limited enlargement of the generally referred to as hard time windows. The service of a customer can be started before or after the hard time window at a penalty cost. The addressed problem thus requires the determination of a sequence of

Strong Lagrangian duality holds for the quadratic programming with a two-sided quadratic constraint. In this paper, we show that the two-sided quadratic constrained quadratic fractional programming, if well scaled, also has zero Lagrangian duality gap. However, this is not always true without scaling. For a special case, the identical regularized total least squares problem, we establish the necessary

We study a problem in which at least a given quantity of a single product has to be partitioned into lots, and lots have to be assigned to the unrelated parallel machines for processing so that the maximum machine completion time or the sum of machine completion times is minimized. Machine dependent lower and upper bounds on the lot size are given. The product can be continuously divisible or discrete