In this paper, we present a stochastic augmented Lagrangian approach on (possibly infinite-dimensional) Riemannian manifolds to solve stochastic optimization problems with a finite number of deterministic constraints. We investigate the ...

Time-varying polytope distance (TVPD) problems are prevalent in scientific and engineering applications and can be transformed into time-varying quadratic programming (TVQP) problems with both equality and inequality constraints. Concurrently, the ...${\mathrm{}}^{\mathrm{}}$${\mathrm{}}^{\mathrm{}}$

In this paper, the mixed equilibrium problem is studied by employing a continuous-time multi-agent system, where the objective of agents is to cooperatively find a point from the feasible set such that the sum of bifunctions with a free-variable ...

This paper studies the constrained distributed resource allocation problems (DRAPs) of autonomous multi-agent systems (MASs). Unlike well-defined DRAPs, the disturbed second-order agents are taken into account. Moreover, all agents' decisions are ...

In this paper we derive the equations of motion for nonholonomic systems subject to inequality constraints, both in continuous-time and discrete-time. The last is done by discretizing the continuous time-variational principle which defines the ...

We study nonlinear optimization problems with a stochastic objective and deterministic equality and inequality constraints, which emerge in numerous applications including finance, manufacturing, power systems and, recently, deep neural networks. ...

This paper studies distributed resource allocation problems of multi-agent systems, where the decisions of agents are subject to inequality network resource constraints and local inequality constraints. Compared with well-known ...

Multiple instance learning (MIL) deals with data grouped into bags of instances, of which only the global information is known. In recent years, this weakly supervised learning paradigm has become very popular in histological image analysis ...

• A novel constrained formulation for weakly supervised histology WSI classification.
• Using relative class proportions priors for prostate cancer instances grading.
• SICAP-MIL, a novel publicly available dataset of prostate WSIs for ...

Nonlinear and nonconvex optimization problem (NNOP) is a challenging problem in control theory and applications. In this paper, a novel mixture varying-gain dynamic learning network (MVG-DLN) is proposed to solve NNOP with inequality ...

Stochastic Configuration Networks (SCNs) can be incrementally constructed by using supervisory mechanisms on the selection of random weights and biases. Due to its ease in implementation, fast training and less human intervention, SCNs ...

Piecewise polynomial constraint systems are common in numerous problems in computational geometry, such as constraint programming, modeling, and kinematics. We propose a framework that is capable of decomposing, and efficiently solving ...

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• A framework for decomposing constraint systems, with a total DoF of zero or one.

In this paper, interpolating curve or surface with linear inequality constraints is considered as a general convex optimization problem in a Reproducing Kernel Hilbert Space. The aim of the present paper is to propose an approximation method in a very ...

In this paper, we address the nonconvex optimization problem, with the goal function and the inequality constraints given by the functions represented by the difference of convex functions. The effectiveness of the classical Lagrange function and the ...

In this paper, a primal-dual quasi interior-point algorithm for inequality constrained optimization problems is presented. At each iteration, the algorithm solves only two or three reduced systems of linear equations with the same coefficient matrix. The ...

This paper introduces computational techniques to support architects and structural designers in the shaping of strut-and-tie networks in static equilibrium. Taking full advantage of geometry, these techniques build on the reciprocal diagrams of graphic ...

Support vector regression (SVR) techniques are aimed at discovering a linear or nonlinear structure hidden in sample data. Most existing regression techniques take the assumption that the error distribution is Gaussian. However, it was observed that the ...

In the RC association model for a two-way contingency table, it is often natural to impose order constraints on the score parameters of the row and column variables. In this article, a simple and efficient Bayesian model selection procedure is proposed ...

Under the framework of switched systems, this paper considers a multi-proportional-integral-derivative controller parameter tuning problem with terminal equality constraints and continuous-time inequality constraints. The switching time and controller ...

We consider a generalization of the linear optimization problem with fuzzy relational (in)equality constraints by allowing for bipolar max-min constraints, i.e. constraints in which not only the independent variables but also their negations occur. A ...

A Bayesian approach is developed for selecting the model that is most supported by the data within a class of marginal models for categorical variables, which are formulated through equality and/or inequality constraints on generalized logits (local, ...