Search arXiv⌕ Search

arXiv · 2610.13141

Representing the Non-dominated Set of Multi-objective Optimization Problems with Polyhedral Ordering Cones

Abstract

In the literature, research on representing the non-dominated set has focused almost exclusively on multi-objective optimization problems with Pareto-dominance. However, general ordering cones are used to incorporate preferences of decision makers into the optimization process and to model other specific types of optimization problems, such as ordinal optimization problems. In this work, we consider several representation approaches based on representation relations in the context of multi-objective optimization problems with polyhedral ordering cones. Our main research question is whether existing theory and solution approaches developed for the Pareto case can be used to address the more general setting.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Chiara Weuste, Michael Stiglmayr. 2026-10-09. Representing the Non-dominated Set of Multi-objective Optimization Problems with Polyhedral Ordering Cones. https://arxiv.org/abs/2610.13141

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

On the existence of infinitely many realization functions of non-global local minima for artificial neural networks with ReLU activation

Gradient descent (GD) type optimization schemes are standard tools for training fully connected feedforward artificial neural networks (ANNs) with rectified linear unit (ReLU) activation and can be considered as temporal discretizations of solutions of gradient flow (GF) differential equations. It is known that, for shallow ReLU ANNs, the risk of every bounded GF trajectory converges under suitable assumptions to the risk of a critical point, a zero of the corresponding gradient function. This motivates studying necessary and sufficient conditions in GF and GD convergence analysis for critical points and their dependence on problem parameters, such as the target function. In the first main result, we prove for shallow ReLU ANNs that for every dimension $d \in \mathbb{N}$, every $a \in \mathbb{R}$, $b \in (a, \infty)$, and every positive $δ\in (0, \infty)$, there exists a Lipschitz continuous target function $f \colon [a, b]^d \to \mathbb{R}$ such that, for every hidden-layer width $\mathfrak{h} \in \mathbb{N} \cap (1, \infty)$, there are uncountably many different realization functions of non-global local minimum points whose risks exceed the infimum of the risk function by more than $δ$. In the second main result, we show that, for shallow ReLU ANNs with only one neuron on each layer and a continuous piecewise polynomial target function, there exist at most finitely many different realization functions of critical points.

math.OC↗

Error Bounds and a Smoothing Penalty Method for Binary Orthogonal Optimization

Binary orthogonal optimization arises in semantic hashing, orthogonal binary projection, and Hadamard matrix design. Existing methods often handle binarity and orthogonality separately, so their outputs need not satisfy both constraints simultaneously and generally come without a residual-based guarantee of exact feasibility. Using an exact box-constrained reformulation, we establish local and global Lipschitz error bounds and prove that the Lipschitz exponent is optimal. In particular, when the binary orthogonal feasible set is nonempty, any box-constrained matrix whose orthogonality residual is below $1$ admits a feasible sign rounding, and the rounding distance is bounded by this residual. These bounds also yield a globally exact nonsmooth orthogonality-residual penalty over the box and a uniformly sharp local penalty threshold. We further develop a smoothing penalty method for binary orthogonal optimization (SPBO). We prove that every accumulation point is stationary for the limiting nonsmooth penalty problem. Numerical experiments on orthogonal binary projection, supervised hashing, and Hadamard completion show that SPBO substantially reduces orthogonality violations while retaining competitive objective values or retrieval accuracy.

math.OC↗

Consumption adjustment with mean-reverting risk premia: A singular stochastic control approach

We study a two-sided singular stochastic control problem in which changes in consumption carry utility costs and the risk premium follows the mean-reverting Kim-Omberg model. Optimal consumption is reflected between two moving barriers given by thresholds that depend on the current risk premium. These thresholds are the stopping boundaries of a Dynkin game for a two-dimensional diffusion driven by a single Brownian motion. A sharp occupation estimate shows that they are Lipschitz in logarithmic scale, uniformly in the cost of reducing consumption, and that smooth fit holds along the diffusion direction at every contact point. Outside a bounded interval of risk premia and at most two further points, the thresholds are infinitely differentiable. As the reduction cost grows, optimal consumption converges to the ratcheting policy.

math.OC↗