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Despite the rich existing literature about minimax optimization in continuous settings, only very partial results of this kind have been obtained for combinatorial settings. In this paper, we fill this gap by providing a characterization of…

Machine Learning · Computer Science 2023-05-29 Loay Mualem , Ethan R. Elenberg , Moran Feldman , Amin Karbasi

In this work, we consider a nonsmooth minimisation problem in which the objective function can be represented as the maximum of finitely many smooth ``subfunctions''. First, we study a smooth min-max reformulation of the problem. Due to…

Optimization and Control · Mathematics 2024-04-17 Charl Ras , Matthew Tam , Daniel Uteda

Let k be a field. A finite dimensional k-algebra is said to be minimal representation-infinite provided it is representation-infinite and all its proper factor algebras are representation-finite. Our aim is to classify the special biserial…

Representation Theory · Mathematics 2011-02-22 Claus Michael Ringel

Motivated by resource allocation problems (RAPs) in power management applications, we investigate solutions to optimization problems that simultaneously minimize an entire class of objective functions. It is straightforward to show…

Optimization and Control · Mathematics 2023-02-28 Martijn H. H. Schoot Uiterkamp

This paper studies the probabilistic function approximation problem over reproducing kernel Hilbert spaces. We show the existence and uniqueness of the optimizer under mild assumptions. Furthermore, we generalize the celebrated representer…

Functional Analysis · Mathematics 2025-07-16 Dongwei Chen , Kai-Hsiang Wang

We distinguish two kinds of piecewise linear functions and provide an interesting representation for a piecewise linear function between two normed spaces. Based on such a representation, we study a fully piecewise linear vector…

Optimization and Control · Mathematics 2020-09-23 Xiyin Zheng , Xiaoqi Yang

Submodular functions are an important class of functions in combinatorial optimization which satisfy the natural properties of decreasing marginal costs. The study of these functions has led to strong structural properties with applications…

Multiagent Systems · Computer Science 2009-11-13 Gagan Goel , Pushkar Tripathi , Lei Wang

The Tikhonov-Phillips method is widely used for regularizing ill-posed inverse problems mainly due to the simplicity of its formulation as an optimization problem. The use of different penalizers in the functionals associated to the…

Functional Analysis · Mathematics 2011-08-23 Gisela L. Mazzieri , Ruben D. Spies , Karina G. Temperini

There are many significant applied contexts that require the solution of discontinuous optimization problems in finite dimensions. Yet these problems are very difficult, both computationally and analytically. With the functions being…

Optimization and Control · Mathematics 2023-05-25 Ying Cui , Junyi Liu , Jong-Shi Pang

A k-dissimilarity map on a finite set X is a function D : X \choose k \rightarrow R assigning a real value to each subset of X with cardinality k, k \geq 2. Such functions, also sometimes known as k-way dissimilarities, k-way distances, or…

Combinatorics · Mathematics 2014-12-23 Sven Herrmann , Vincent Moulton

We establish formulas for the number of all downsets (or equivalently, of all antichains) of a finite poset P. Then, using these numbers, we determine recursively and explicitly the number of all posets having a fixed set of minimal points…

Combinatorics · Mathematics 2018-02-06 Frank A Campo , Marcel Erné

We consider the following class of submodular k-multiway partitioning problems: (Sub-$k$-MP) $\min \sum_{i=1}^k f(S_i): S_1 \uplus S_2 \uplus \cdots \uplus S_k = V \mbox{ and } S_i \neq \emptyset \mbox{ for all }i\in [k]$. Here $f$ is a…

Data Structures and Algorithms · Computer Science 2021-05-11 Richard Santiago

A real-valued set function is (additively) approximately submodular if it satisfies the submodularity conditions with an additive error. Approximate submodularity arises in many settings, especially in machine learning, where the function…

Data Structures and Algorithms · Computer Science 2020-10-08 Flavio Chierichetti , Anirban Dasgupta , Ravi Kumar

In this work, we propose using a unified representation, termed Factorized Features, for low-level vision tasks, where we test on Single Image Super-Resolution (SISR) and \textbf{Image Compression}. Motivated by the shared principles…

Image and Video Processing · Electrical Eng. & Systems 2025-11-04 Yang-Che Sun , Cheng Yu Yeo , Ernie Chu , Jun-Cheng Chen , Yu-Lun Liu

A $k$-submodular function is a generalization of the submodular set function. Many practical applications can be modeled as maximizing a $k$-submodular function, such as multi-cooperative games, sensor placement with $k$ type sensors,…

Combinatorics · Mathematics 2023-12-13 Hongyang Zhang , Wenchang Luo

In this paper we present a new class of complexity measures, induced by a new data structure for representing $k$-valued functions (operations), called minor decision diagram. The results are presented in terms of Multi-Valued Logic…

Discrete Mathematics · Computer Science 2016-11-18 Slavcho Shtrakov

We investigate a more generalized form of submodular maximization, referred to as $k$-submodular maximization, with applications across social networks and machine learning domains. In this work, we propose the multilinear extension of…

Data Structures and Algorithms · Computer Science 2023-09-13 Lingxiao Huang , Baoxiang Wang , Huanjian Zhou

In this manuscript, we offer a gentle review of submodularity and supermodularity and their properties. We offer a plethora of submodular definitions; a full description of a number of example submodular functions and their generalizations;…

Machine Learning · Computer Science 2022-10-05 Jeff Bilmes

A family of simplicial complexes, connected with simplicial maps and indexed by a poset $P$, is called a poset tower. The concept of poset towers subsumes classical objects of study in the persistence literature, as, for example,…

Algebraic Topology · Mathematics 2025-05-14 Tamal K. Dey , Florian Russold

We present a practical and powerful new framework for both unconstrained and constrained submodular function optimization based on discrete semidifferentials (sub- and super-differentials). The resulting algorithms, which repeatedly compute…

Data Structures and Algorithms · Computer Science 2013-08-13 Rishabh Iyer , Stefanie Jegelka , Jeff Bilmes
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